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Core, social and moral disgust processing in youth with autism spectrum disorder (ASD)
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Core, social and moral disgust processing in youth with autism spectrum disorder (ASD)
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Copyright 2024 Aditya Jayashankar
CORE, SOCIAL AND MORAL DISGUST PROCESSING IN YOUTH WITH AUTISM
SPECTRUM DISORDER (ASD)
by
Aditya Jayashankar
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(OCCUPATIONAL SCIENCE)
May 2024
ii
ACKNOWLEDGEMENTS
I am profoundly grateful to my advisor, Dr. Lisa Aziz-Zadeh, whose steady support,
insightful guidance, and boundless patience have been instrumental in every step of this
dissertation. Her expertise and encouragement have inspired me to push the boundaries of my
research and strive for excellence.
I extend my sincere appreciation to my dissertation committee members, Dr. Jonas
Kaplan, Dr. Sharon Cermak and Dr. Mary Helen Immordino-Yang, for their valuable feedback,
constructive criticism, and scholarly insights, which have significantly enriched the quality of
this work.
To my family, my Mama and Papa, I owe a debt of gratitude for their unconditional love,
unwavering belief, and endless encouragement throughout this journey. Their sacrifices and
unwavering support have been the foundation of my success.
My deepest thanks go to my wife Surbhi, whose continuous love, patience, and
understanding have sustained me through the highs and lows of this challenging endeavor. Her
unyielding belief in me and unending support have been my greatest blessings.
I am also grateful to my friends and CeNEC colleagues for their encouragement,
camaraderie, and shared experiences, which have provided solace and inspiration during the
difficult moments.
Lastly, I extend my heartfelt appreciation to all the participants who generously
contributed their time and patience to this research.
This dissertation is a testament to the collective efforts and support of many individuals,
and for that, I am truly humbled and grateful.
iii
TABLE OF CONTENTS
ACKNOWLEDGEMENTS............................................................................................................ ii
LIST OF TABLES........................................................................................................................ vii
LIST OF FIGURES ...................................................................................................................... vii
ABSTRACT................................................................................................................................. xiii
CHAPTER 1: LITERATURE REVIEW.........................................................................................1
1.1. Introduction...........................................................................................................................1
1.2. Definition of disgust .............................................................................................................3
1.3. Domains of Disgust ..............................................................................................................5
1.3.1. Core disgust ...................................................................................................................7
1.3.2. Animal-reminder disgust ...............................................................................................8
1.3.3. Socio-moral disgust .......................................................................................................9
1.4. Disgust processing and disgust behaviors ..........................................................................11
1.4.1. Disgust proneness ........................................................................................................11
1.4.2. Vicarious disgust learning in children .........................................................................12
1.4.3. Disgust-related behavioral responses...........................................................................14
1.4.4. Disgust and Cognitive Bias..........................................................................................15
1.4.4. Disgust and Moral processing......................................................................................17
1.5. Neuroimaging studies.........................................................................................................19
1.5.1. Insula............................................................................................................................20
1.5.2. Social disgust ...............................................................................................................22
1.5.3. Disgust Mirroring.........................................................................................................23
1.5.4. Moral disgust ...............................................................................................................23
1.6. Disgust experiences of the pediatric ASD population........................................................28
1.6.1. Core disgust in ASD ....................................................................................................29
1.6.2. Facial recognition of disgust in ASD...........................................................................30
1.6.3. Sociomoral disgust in ASD..........................................................................................32
1.7. Future Directions ................................................................................................................35
CHAPTER 2: NEURAL CORRELATES OF PHYSICAL AND SOCIAL
DISGUST PROCESSING IN YOUTH WITH AUTISM (ASD)..................................................41
Abstract......................................................................................................................................41
2.1. Introduction.........................................................................................................................43
2.1.1. Food-related disgust in autism.....................................................................................43
2.1.2. Recognition of disgusted facial expression in autism..................................................45
2.1.3. Present study ................................................................................................................46
2.2. Materials and Methods........................................................................................................47
2.2.1. Participants...................................................................................................................47
2.2.2. Behavioral measures....................................................................................................48
2.2.3. MRI protocol................................................................................................................50
2.2.4. fMRI task .....................................................................................................................50
iv
2.2.5. fMRI data analysis.......................................................................................................52
2.2.6. Statistical analysis........................................................................................................55
2.3. Results.................................................................................................................................56
2.3.1. Behavioral & demographic variables...........................................................................56
2.3.2. Between-group differences in fMRI............................................................................60
1. Whole-brain voxel-wise activation differences.........................................................60
2. Small volume correction............................................................................................61
a. Disgusting foods....................................................................................................61
b. Disgusted facial expressions..................................................................................65
2.3.3. Brain-behavior correlations across groups...................................................................68
2.3.4. Partial correlations and regression analyses ................................................................71
2.4. Discussion...........................................................................................................................75
2.4.1. Between-group behavioral differences on disgust proneness and relation
with other behaviors...............................................................................................................75
2.4.2. Decreased emotion-related brain activity in the ASD group for Physical
disgust (Disgusting foods) .....................................................................................................77
2.4.3. Activity in the ASD group for Social disgust (Disgusted facial
expressions)............................................................................................................................79
2.4.5. Regions showing differences in ASD for both Physical and Social
disgust ....................................................................................................................................82
2.4.6. Limitations...................................................................................................................85
2.5. Conclusion ..........................................................................................................................86
CHAPTER 3: FUNCTIONAL CONNECTIVITY DIFFERENCES DURING
PHYSICAL AND SOCIAL DISGUST PROCESSING IN AUTISM..........................................88
Abstract......................................................................................................................................88
3.1. Introduction.........................................................................................................................89
3.1.1. Present study ................................................................................................................93
3.2. Methods ..............................................................................................................................94
3.2.1. Participants...................................................................................................................94
3.2.2. Behavioral measures....................................................................................................95
3.2.3. MRI data acquisition protocol .....................................................................................96
3.2.4. fMRI task .....................................................................................................................97
3.2.5. fMRI data analysis.......................................................................................................98
a. Within-subjects preprocessing...................................................................................99
b. Functional connectivity analyses...............................................................................99
3.2.6. Correlational analysis.................................................................................................100
3.3. Results...............................................................................................................................101
3.3.1. Functional connectivity differences during the Disgust Food condition ...................101
a. Right mid-insula (MI)..............................................................................................101
b. Right medial orbitofrontal cortex (mOFC)..............................................................102
v
3.3.2 Functional connectivity differences during the Disgust Faces condition ................105
a. Right mid-insula (MI)..............................................................................................105
b. Left medial orbitofrontal cortex (mOFC) ................................................................105
3.3.3. Brain-behavior correlations in ASD ..........................................................................109
a. Disgust Food condition............................................................................................109
b. Disgust Faces condition ...........................................................................................111
3.4. Discussion.........................................................................................................................114
3.4.1. Hypoconnectivity in emotion-related regions in ASD...............................................115
3.4.2. Hyperconnectivity in emotion-related regions in ASD .............................................116
3.4.3. Connectivity associated with disgust proneness (trait disgust)..................................118
3.4.4. Lateralization of functional connectivity differences ................................................119
3.4.5. Limitations.................................................................................................................121
3.5. Conclusion ........................................................................................................................121
CHAPTER 4: THE INFLUENCE OF DISGUSTING ODORS ON MORAL
DISGUST IN AUTISTIC YOUTH .............................................................................................123
Abstract....................................................................................................................................123
4.1. Introduction.......................................................................................................................125
4.1.1. Moral decision-making in autism ..............................................................................126
4.1.2. The effect of physical disgust on morality in non-autistics.......................................128
4.1.3. Present study ..............................................................................................................129
4.2. Materials and Methods......................................................................................................131
4.2.1. Participants.................................................................................................................131
4.2.2. Task and Environment ...............................................................................................132
4.2.2a. Moral decision-making task.................................................................................132
4.2.2b. Odor vs. No-odor Environment ...........................................................................133
4.2.3. Behavioral measures..................................................................................................134
4.2.4. Statistical analysis......................................................................................................136
4.3. Results...............................................................................................................................137
4.3.1. Behavioral comparisons and correlations in both groups combined .........................137
4.3.2. Comparison of survey ratings between TD and ASD participants across
odor conditions.....................................................................................................................141
4.3.3. Comparison of purity violations ratings between participants accounting
for potential covariates.........................................................................................................144
1. DP and gustatory sensitivity ....................................................................................144
2. DP and interoception (heartbeat counting) ..............................................................145
3. DP and alexithymia..................................................................................................146
4.4. Discussion.........................................................................................................................147
4.4.1. Disgust priming influences purity violation moral ratings in ASD ...........................148
4.4.2. Relationship between behavioral variability and moral ratings.................................149
4.4.3. Significant differences in participant ratings for each vignette type .........................150
vi
4.4.4. Limitations.................................................................................................................151
4.5. Conclusion ........................................................................................................................152
CHAPTER 5: GRAND DISCUSSION .......................................................................................154
5.1. Elevated disgust traits in autistic youth ............................................................................156
5.2. Mid-insula as a possible hub for disgust-related processing ............................................159
5.3. Moral disgust decision-making is influenced by physical disgust in autism....................161
5.4. An updated model of altered disgust processing in autism ..............................................162
5.5. Occupational science lens for disgust processing research in autism...............................167
5.6. Limitations, Future directions & Conclusion ...................................................................169
BIBLIOGRAPHY........................................................................................................................172
APPENDICES .............................................................................................................................214
Appendix i: Supplementary Figures........................................................................................214
Appendix ii. Pilot Survey of Moral Foundations Theory (MFT) Questionnaire.....................217
1. Methods............................................................................................................................217
1a. Participants.................................................................................................................217
1b. Pilot survey ................................................................................................................217
1c. Final selection of questions........................................................................................218
2. Results..............................................................................................................................220
2a. Moral vignettes...........................................................................................................220
2b. Physically disgusting vignettes..................................................................................221
2c. Neutral negative vignettes..........................................................................................221
Appendix iii. Analysis of covariance models for Disgust Sensitivity (DS),
adjusting for Gustatory sensitivity, Interoception, and Alexithymia.......................................223
1. DS and gustatory sensitivity ............................................................................................223
2. DS and interoception (heartbeat counting) ......................................................................223
3. DS and alexithymia..........................................................................................................224
vii
LIST OF TABLES
Table 1. Description of the different domains in our conceptual model of disgust.........................7
Table 2. Descriptive summary of demographic and behavioral variables and group
comparisons ...........................................................................................................................57
Table 3. Correlation matrix (lower triangle) for Pearson’s product-moment
correlation of main behavioral variables across groups.........................................................59
Table 4. Whole-brain voxel-wise activation results for the main effect of Disgust
Faces ......................................................................................................................................60
Table 5. Small-volume correction voxel-wise activations for the Disgust Food main
effect and the Disgust Food>Neutral Food contrast ..............................................................62
Table 6. Small-volume correction voxel-wise activations for the Disgust Faces main
effect and the Disgust Faces>Neutral Faces contrast ............................................................65
Table 7. Correlation table of Pearson’s correlations of selected Disgust Food and
Disgust Faces ROIs and main behavioral and demographic variables..................................70
Table 8. Partial correlation coefficients for associations between the right midinsula (MI) and right medial orbitofrontal cortex (mOFC) with disgust
sensitivity (DS) and propensity (DP) during Disgust Food condition, adjusting
for SEQ gustatory hypersensitivity, SEQ tactile hypersensitivity, AQC 2-factor
score, sex, and IQ; and the left mOFC and the right MI with disgust sensitivity
(DS) and propensity (DP) during Disgust Faces condition, adjusting for AQC
2-factor...................................................................................................................................72
Table 9. Seed-to-voxel activations for the Disgust Food condition for the seeds:
right mid-insula (MI) and right medial orbitofrontal cortex (mOFC; p<0.05,
SVC) ....................................................................................................................................102
Table 10. Seed-to-voxel activations for the Disgust Faces condition for the seeds:
right mid-insula (MI) and left medial orbitofrontal cortex (mOFC; p<0.05,
SVC) ....................................................................................................................................106
Table 11. Sample vignette (purity violation) and questions for perceived wrongness,
necessary punishment and permissibility (Modified from Dempsey et al., 2022
and Schnall, Haidt, et al., 2008)...........................................................................................133
Table 12. Descriptive summary of demographic and behavioral variables and group
comparisons .........................................................................................................................138
Table 13. Group comparisons on survey responses of perceived wrongness,
deserved punishment, and permissibility collapsed across smell and no smell
conditions.............................................................................................................................142
viii
LIST OF FIGURES
Figure 1. Conceptual model of the overlap between the different domains of disgust ...................6
Figure 2. Conceptual model of the relationship between disgust, sensory and moral
processing regions in the brain and the hypothesized effect of Autism Spectrum
disorder (ASD) symptomatology on these functional connections. Functional
links are shown in black arrows with triangular tips, somatic state linkage is
shown with a red arrow with diamond tips, and the effect of ASD symptoms are
shown in orange dashed arrows with rounded tips. ACC, anterior cingulate
cortex; VMPFC, ventromedial prefrontal cortex; DLPFC, dorsolateral
prefrontal cortex; PCC, posterior cingulate cortex; SMA, supplementary motor
area. Adapted from somatic marker hypothesis models in Bechara, 2013; Koob,
Arends, McCracken, & Le Moal, 2019; Saive, Royet, & Plailly, 2014. ...............................27
Figure 3. Examples of fMRI stimuli divided into the four categories - disgusting
foods and faces, and neutral foods and faces.........................................................................52
Figure 4. A. Correlation plot (lower triangle) of main behavioral variables across
groups. B. Correlations in TD group. C. Correlations in ASD group. DS =
Disgust sensitivity; DP = Disgust propensity; DES = Disgust Emotion Scale;
SEQ = Sensory Experiences Questionnaire version 3.0; Hyper =
hypersensitivity; Hypo = hyposensitivity; SIRS = sensory interests, repetitions
and seeking; EP = enhanced perception; BPQ = Body Perception Questionnaire
- Very Short Form; AQC = Alexithymia Questionnaire for Children; ID =
difficulty identifying feelings; Comm = difficulty describing feelings; NEPSY =
NEuroPSYchological behavioral assessment; ToM = Theory of mind.................................58
Figure 5. Cross-sectional view along the sagittal axis (right-to-left) of whole-brain
activation results for the contrast of ASD>TD for the main effect of Disgust
Faces, thresholded at Z>3.1, cluster p<0.05 ..........................................................................61
Figure 6. A. Cross-sectional view along the sagittal axis (left-to-right) of smallvolume correction (SVC) activation results for both contrast: TD>ASD (redyellow) and ASD>TD (blue-light blue) during the Disgust Food>Rest condition
at Z>1.96, cluster p uncorrected. B. Nodes representing the ROIs chosen from
SVC cluster activations for contrasts TD>ASD (blue) and ASD>TD (red)
during Disgust Food. dAI = dorsal anterior insula; vAI = ventral anterior insula;
MI = mid-insula; PI = posterior insula; Amyg = amygdala; mOFC = medial
orbitofrontal cortex; vmPFC = ventromedial prefrontal cortex. C. Glass brain
representation of the left Disgust Food ROIs, corresponding to the nodes in B.,
with cut-out for insular regions. D. Glass brain representation of the right
Disgust Food ROIs, corresponding to the nodes in B., with cut-out for insular
regions....................................................................................................................................64
Figure 7. A. Cross-sectional view along the sagittal axis (left-to-right) of smallvolume correction (SVC) activation results for both contrast: TD>ASD (redyellow) and ASD>TD (blue-light blue) during the Disgust Faces>Rest
condition at Z>1.96, cluster p uncorrected. B. Nodes representing the ROIs
chosen from SVC cluster activations for contrasts TD>ASD (blue) and
ix
ASD>TD (red) during Disgust Faces. dAI = dorsal anterior insula; vAI =
ventral anterior insula; MI = mid-insula; PI = posterior insula; Amyg =
amygdala; mOFC = medial orbitofrontal cortex; vmPFC = ventromedial
prefrontal cortex; ACC = anterior cingulate cortex. C. Glass brain
representation of the left Disgust Faces ROIs, corresponding to the nodes in B.,
with cut-out for insular regions. D. Glass brain representation of the right
Disgust Faces ROIs, corresponding to the nodes in B., with cut-out for insular
regions....................................................................................................................................67
Figure 8. A. Correlation plot of Pearson’s correlations between selected Disgust
Food ROIs and main behavioral and demographic variables. B. Correlation plot
of Pearson’s correlations between selected Disgust Faces ROIs and main
behavioral and demographic variables...................................................................................69
Figure 9. Scatter plots with regression lines across groups (left) and for each group
(right) for the Disgust Food condition of the relationship of, A. the right MI and
disgust sensitivity; B. the right mOFC and disgust sensitivity; C. the right
mOFC and disgust propensity................................................................................................73
Figure 10. Scatter plots with regression lines across groups (left) and for each group
(right) for the Disgust Faces condition of the relationship of, A. the right MI and
disgust propensity; B. the left mOFC and disgust sensitivity................................................74
Figure 11. Psychophysiological interaction (PPI) results for co-activations (nodes)
and connections (edges) of the right mid-insula (A) and the right medial
orbitofrontal cortex (B) in the Disgust Food condition. Hypoconnectivity in
ASD and TD>ASD co-activations activations appear in red. dAI = dorsal
anterior insula; vAI = ventral anterior insula; MI = mid-insula; PI = posterior
insula; vmPFC = ventromedial prefrontal cortex; OFC = orbitofrontal cortex;
mOFC = medial OFC; Amyg = amygdala; Put = putamen; Pall = pallidum.......................104
Figure 12. Psychophysiological interaction (PPI) results for co-activations (nodes)
and connections (edges) of the right mid-insula (A) and the left medial
orbitofrontal cortex (B) in the Disgust Faces condition. Hypoconnectivity in
ASD and TD>ASD co-activations appear in blue, while hyperconnectivity in
ASD and ASD>TD co-activations appear in red. dAI = dorsal anterior insula;
vAI = ventral anterior insula; MI = mid-insula; PI = posterior insula; vmPFC =
ventromedial prefrontal cortex; mOFC = medial orbitofrontal cortex; Amyg =
amygdala; Put = putamen; ACC = anterior cingulate cortex; MCC = middle
cingulate cortex....................................................................................................................108
Figure 13. A. Correlation plot of Pearson’s correlations between selected right midinsula (MI) ROIs during the Disgust Food condition and main behavioral and
demographic variables in the ASD group. B. Correlation plot of Pearson’s
correlations between selected right medial orbitofrontal cortex (mOFC) ROIs
during the Disgust Food condition and main behavioral and demographic
variables in the ASD group. Note: DS = Disgust sensitivity; DP = Disgust
propensity; BPQ = Body Perception Questionnaire - Very Short Form; AQC =
Alexithymia Questionnaire for Children; NEPSY = NEuroPSYchological
behavioral assessment; ToM = Theory of mind; ADOS = Autism Diagnostic
x
Observation Schedule 2nd edition; SA = Social affect; RRB = Restricted and
repetitive behaviors..............................................................................................................110
Figure 14. Scatter plots with regression lines across groups (black) and for each
group (TD: green circles; ASD: orange triangles) for the Disgust Food
condition for disgust sensitivity and propensity with the connectivity between
the right mid-insula and the left dorsal anterior insula (dAI). Note, correlations
are only significant in the ASD group (p<0.05). .................................................................111
Figure 15. A. Correlation plot of Pearson’s correlations between selected right midinsula (MI) ROIs during the Disgust Faces condition and main behavioral and
demographic variables in the ASD group. B. Correlation plot of Pearson’s
correlations between selected left medial orbitofrontal cortex (mOFC) ROIs
during the Disgust Faces condition and main behavioral and demographic
variables in the ASD group. Note: DS = Disgust sensitivity; DP = Disgust
propensity; BPQ = Body Perception Questionnaire - Very Short Form; AQC =
Alexithymia Questionnaire for Children; NEPSY = NEuroPSYchological
behavioral assessment; ToM = Theory of mind; ADOS = Autism Diagnostic
Observation Schedule 2nd edition; SA = Social affect; RRB = Restricted and
repetitive behaviors..............................................................................................................112
Figure 16. A. Scatter plots with regression lines across groups (black) and for each
group (TD: green circles; ASD: orange triangles) for the Disgust Faces
condition for disgust sensitivity and the connectivity between the right midinsula and left ventromedial prefrontal cortex (vmPFC). B. Scatter plots with
regression lines across groups (black) and for each group (TD: green circles;
ASD: orange triangles) for the Disgust Faces condition for disgust sensitivity
and propensity with the connectivity between the right mid-insula and the right
amygdala. Note, correlations are only significant in the ASD group (p<0.05). ..................113
Figure 17. A. Correlation plot (lower triangle) of main behavioral variables in both
groups and both conditions combined. B. Correlations of wrongness ratings
with behavioral variables in both groups and both conditions combined. C.
Correlations of punishment ratings with behavioral variables in both groups and
both conditions combined. D. Correlations of permissibility ratings with
behavioral variables in both groups and both conditions combined. DS =
Disgust sensitivity; DP = Disgust propensity; SEQ = Sensory Experiences
Questionnaire version 3.0; Hyper = hypersensitivity; Hypo = hyposensitivity;
AQ = Autism Spectrum Quotient; BPQ = Body Perception Questionnaire -
Very Short Form; Mean HB diff = Mean absolute mismatch in heartbeat
counting task; AQC = Alexithymia Questionnaire for Children. ........................................140
Figure 18. Distribution of the survey responses comparison the odor (blue) and nonodor (red) conditions on perceived wrongness (A, D), necessary punishment (B,
E), and permissibility (C, F) of actions across the different vignette types in the
TD (A to C) and the ASD (D to F) groups. .........................................................................143
Figure 19. Updated hypoconnected conceptual model of the relationship between
disgust, sensory and moral processing regions in the brain and the observed
activation and connectivity patterns in Autism Spectrum disorder (ASD) and the
xi
effect of ASD symptomatology on these functional connections.
Hypoconnectivity links are shown in blue arrows with triangular tips, lower
disgust-related activations (TD>ASD) in right mid-insula and right medial
orbitofrontal cortex (mOFC) in ASD are shown in a blue block, higher disgustrelated activations (ASD>TD) in the left mOFC in ASD are shown in a red
block, and the effect of ASD symptoms are shown in orange dashed arrows
with rounded tips. AI, anterior insula; OFC, orbitofrontal cortex; ACC, anterior
cingulate cortex; MCC, middle cingulate cortex; VMPFC, ventromedial
prefrontal cortex; DLPFC, dorsolateral prefrontal cortex; PCC, posterior
cingulate cortex; SMA, supplementary motor area; Physical, activity/connection
during physical disgust; Social; activity/connection during social disgust; Both;
activity/connection during both physical and social disgust. Adapted from
somatic marker hypothesis models in Bechara, 2013; Koob, Arends,
McCracken, & Le Moal, 2019; Saive, Royet, & Plailly, 2014. ...........................................164
Figure 20. Updated hyperconnected conceptual model of the relationship between
disgust, sensory and moral processing regions in the brain and the observed
activation and connectivity patterns in Autism Spectrum disorder (ASD) and the
effect of ASD symptomatology on these functional connections.
Hyperconnectivity links are shown in red arrows with triangular tips, lower
disgust-related activations (TD>ASD) in right mid-insula and right medial
orbitofrontal cortex (mOFC) in ASD are shown in a blue block, higher disgustrelated activations (ASD>TD) in the left mOFC in ASD are shown in a red
block, and the effect of ASD symptoms are shown in orange dashed arrows
with rounded tips. AI, anterior insula; OFC, orbitofrontal cortex; ACC, anterior
cingulate cortex; MCC, middle cingulate cortex; VMPFC, ventromedial
prefrontal cortex; DLPFC, dorsolateral prefrontal cortex; PCC, posterior
cingulate cortex; SMA, supplementary motor area; Physical, activity/connection
during physical disgust; Social; activity/connection during social disgust; Both;
activity/connection during both physical and social disgust. Adapted from
somatic marker hypothesis models in Bechara, 2013; Koob, Arends,
McCracken, & Le Moal, 2019; Saive, Royet, & Plailly, 2014. ...........................................166
Supplementary Figure S1. Neural regions from the NeuroSynth meta-analysis maps,
including structural parcellations of insular sub-regions (negative slices = left
side, positive slices = right side). Regions of interest (ROIs) included dorsal and
ventral anterior insula (slices -40 to -28, and 30 to 42); mid-insula and posterior
insula (slices -40, -34, 36, 42); anterior cingulate cortex (slices -16, -10, 6, 12);
amygdala (slices -28 to -10, and 6 to 36); medial orbitofrontal cortex and
ventromedial prefrontal cortex (slices -28 to 30); and fusiform areas (slices -40,
-34, 36, 42)...........................................................................................................................214
Supplementary Figure S2. Psychophysiological interaction (PPI) effects during the
Disgust Food condition for the connectivity of A. the right mid-insula (MI), and
B. the right medial orbitofrontal cortex (mOFC).................................................................215
Supplementary Figure S3. Psychophysiological interaction (PPI) effects during the
Disgust Faces condition for the connectivity of A. the right mid-insula (MI),
and B. the left medial orbitofrontal cortex (mOFC). ...........................................................216
xii
Supplementary Figure S4. Examples of pilot survey questions for moral violation
vignettes (A), physically disgusting vignettes (B), and neutral negative vignettes
(C). .......................................................................................................................................219
xiii
ABSTRACT
Autistic youth exhibit divergent disgust processing, which may manifest as differences in
behaviors towards contaminating stimuli (core/physical disgust) and differences in vicarious
socio-emotional processing (social disgust). Furthermore, autistic youth tend to display relatively
more outcome-based moral judgments, leading to stronger moral feelings and attributions of
punishment for unintentional, unexplained moral violations. In non-autistic individuals, there is
an established link between physical disgust processing and moral judgments (moral disgust),
but the relationship remains unexplored in autism. This dissertation synthesized existing
literature to underscore the potential consequences of differing disgust processing in autism,
including increased susceptibility to illness and gastrointestinal issues, and restricted social
communication and vicarious learning of disgust stimuli. In a series of three studies, this
dissertation aimed to address this gap by elucidating the neurobiological basis of disgust
processing differences in autistic youth in the first and second studies, and by investigating the
linkage of disgust processing and moral judgments in autism in the third study. Our findings shed
light on the nuanced activity and connectivity patterns underlying the physical and social disgust
processing differences observed in autistic youth, especially concerning the right mid-insula; and
delineated the influence of these physical disgust processing differences on feelings of moral
disgust in autism. Moreover, by enhancing our understanding of disgust processing and its
implications for social functioning, this study may pave the way for novel interventions aimed at
improving the quality of life for autistic youth.
1
CHAPTER 1: LITERATURE REVIEW
1.1. Introduction
“And here, obviously, you are at an impassable barrier. For no feeling of like or dislike is quite
so fundamental as a physical feeling. Race-hatred, religious hatred, differences of education, of
temperament, of intellect, even differences of moral code, can be got over; but physical repulsion
cannot.” (Orwell, 1958, p. 128)
In his book “The road to Wigan Pier”, George Orwell suggested that the strongest factor
influencing the treatment of the poor by the English upper class in London was physical disgust.
He stated that this disgust, derived from feelings of how “the lower classes smell” (Orwell, 1958,
p. 128), not only affected the behavior of the upper classes towards the impoverished, but also
undercut efforts to introduce socialist reforms that would have otherwise benefited the British
economy and people. How is it that an emotion like disgust could influence such antagonistic
behavior in people (Nichols, 2002)? How is its effect so powerful that it might override logic or
compassion (Hedblom, 2019)?
Disgust experiences have been studied in many neurological and clinical groups, such as
obsessive-compulsive disorder (OCD) and anxiety disorders (Georgiadis et al., 2020; Knowles et
al., 2018; Sprengelmeyer et al., 1997; Vicario, Rafal, Martino, et al., 2017). However, the literature
on disgust in autism spectrum disorder (ASD) is considerably limited (Vicario, Rafal, Martino, et
al., 2017). Until recently, ASD researchers often studied disgust alongside other basic emotions
and overall emotion processing deficits (Harms et al., 2010; Kalyva et al., 2010; Zhao et al., 2016).
Yet, recent findings have revealed that ASD symptomatology results in different experiences for
people with ASD, especially children. Such differences in lived experience include lower
2
frequency of contamination-related behaviors (for example, avoiding inedible or dirty food items;
Kalyva et al., 2010; Siegal et al., 2011), biases in attentional focus directing focus away from
disgusted facial expressions of other people and hindering early disgust learning (Katarzyna et al.,
2010; Xu et al., 2015), difficulties in emotional face processing affecting vicarious learning of
disgust behaviors from caregivers and/or community (eg. avoiding contaminating foods) (Monk
et al., 2010; Yeung et al., 2020), differences in moral behavior and feelings of contempt associated
with challenges with their intention understanding ability, which could further deepen difficulties
maintaining social relationships (Dempsey et al., 2020; K. Gray et al., 2012; Li et al., 2014;
Margoni et al., 2019; Margoni & Surian, 2016), and hyposensitivity to disgusting moral triggers
that could lead to immoral and, perhaps, bellicose behavior (Pond Jr. et al., 2012; Winterich et al.,
2014). However, questions still remain regarding the nature of disgust in the ASD population and
the implications of altered processing on adaptive behaviors in children with ASD (Dempsey et
al., 2020). Additionally, a better understanding of potential alterations to the underlying neural
mechanisms is needed, especially those that modulate information processing, behavior, and
adaptive functioning.
Here we begin to try to answer these questions by: 1) defining “disgust”; 2) describing the
different domains of disgust; 3) reviewing aspects of cognition and behavior related to disgust
processing; 4) underscoring the neurobiological underpinnings of disgust processing and
behaviors; and lastly, 5) reviewing the current literature on domain-specific disgust processing in
children with ASD.
3
1.2. Definition of disgust
For centuries, disgust has been used in common language, either in this form or using one
of its many synonyms - revulsion, repugnance, antipathy, loathing, sickening, nauseating,
aversion, etc. (Olatunji & Sawchuk, 2005). The word itself can be dated as far back as 16th-17th
century Europe (Miller, 1998). One of the earliest and well-known descriptions of disgust can be
found in Charles Darwin’s research, in which he noted how disgust “...refers to something
revolting, primarily in relation to the sense of taste, as actually perceived or vividly imagined; and
secondarily to anything which causes a similar feeling, through the sense of smell, touch and even
of eyesight” (Darwin, 1872, p. 254; Darwin & Prodger, 1998, p. 250).
Researchers described disgust as a primary part of the emotional repertoire of a person
(Ekman et al., 2013; Knowles et al., 2019; Olatunji & Sawchuk, 2005). According to the Theory
of Basic Emotions (Ekman, 1992), disgust is one of the core human emotions that is recognized
universally and across many cultures by the unique facial expression associated with the
experience of disgust (Azlan et al., 2017; Ekman, 1992; Ekman et al., 2013; Levenson, 1992;
Vrana, 1993). Researchers believe the disgust emotion evolved in humans to protect the integrity
of one’s body and, by extension, society (Reyes, 2019) and, regardless of the domain of disgust,
result in the same unique disgusted expression (Chapman et al., 2009). For instance, Harrison and
colleagues (2010) describe the disgust emotion as an indicator of the toxicity of certain foods and
as a means to express that feeling to others. Ancient human beings were hunters and gatherers and
those with the trait to identify distasteful or contaminating foods could have been naturally selected
to pass on their genes (Darwin, 1872; Wicker et al., 2003). Antonio Damasio (2011) further
elaborates on the social dimension of disgust by relating it to contempt, calling it a “biological
metaphor for disgust”. In the same way that disgust protects us from toxic substances, contempt
4
engenders a rejection of that which is immoral or toxic behaviors, thus preserving the safety of the
society by enforcing the isolation/avoidance of contempt/disgust elicitors (Damasio, 2011). This
feeling of contempt would have informed early civilizations about the morality of actions, thus
laying the foundation for ancient forms of law and sociocultural norms (Herz, 2013).
Disgust is a negative emotion and can be basically summed up as “bad taste” (Knowles et
al., 2019; Olatunji & Sawchuk, 2005). However, much like the term emotion, there has been much
debate on how to define disgust (Dixon, 2012; Miller Jr, 2016; Olatunji & Sawchuk, 2005; Rozin,
Haidt, McCauley, et al., 1999). Unfortunately, there has been a lack of research on disgust and
associated behaviors (Miller, 1998). According to Miller (1998), disgust was mostly absent from
research and discourse due to its "lack of decorum" (p. 25) as a topic of discussion. Unlike other
negative feelings, the use of disgust by human civilization in maintaining social control and order
and the psychosocial sensitivity to disgust-eliciting topics made disgust a unique, yet unwanted,
topic for "civilized people to talk about" (Miller, 1998, p. 25). Until the early 2000s, disgust was
considerably understudied compared to the other basic emotions (McNally, 2002; Olatunji &
Sawchuk, 2005; Phillips et al., 1998). Since then, research into disgust and its role in adaptive
behavior and psychiatric illness has steadily increased, and with that further investigation into the
nature of disgust (Olatunji & McKay, 2007) and its relationship with social and moral behavior.
Most present-day definitions of disgust are derivations of Angyal’s (1941) description of
it as the repulsive response to the thought of orally ingesting some aversive or offensive material.
This is evident in widely cited definitions of disgust (Davey, 1994; Haidt et al., 1994; Olatunji &
Sawchuk, 2005; Rozin & Fallon, 1987). For instance, Davey (1994) described it as a type of
rejection response motivated by a desire to stay away from potential contamination, revulsion or
nausea. Rozin and Fallon (1987) defined disgust as a “Revulsion at the prospect of (oral)
5
incorporation of an offensive object,” (Rozin & Fallon, 1987, p. 21) and hypothesized that disgust
responses may be an evolution of distaste that serves as a biological imperative to prevent the
ingestion of potentially dangerous substances (Azlan et al., 2017; Haidt et al., 1994; Rozin &
Fallon, 1987). They distinguish distaste from disgust by theorizing that, while both are forms of
food-rejection, distaste is motivated by sensory features of the substance and disgust is felt after a
cognitive evaluation (ideational factors, perceptions, etc.) of the potential threat posed by the
substance (Rozin, Haidt, McCauley, et al., 1999; Rozin & Fallon, 1987). While past researchers
mainly focused on the food-related aspects of disgust, recent work explains that disgust is a
response to more than just contaminating food, but all forms of contaminants (Leathers-Smith &
Davey, 2011; Olatunji et al., 2010).
1.3. Domains of Disgust
As the definition of disgust expanded to include more domains beyond food-related stimuli,
models of disgust evolved to accommodate new elicitors of the emotion. Rozin and colleagues
(2008) suggested a four-factor model that might explain different domains of disgust. According
to their model, disgust elicitors could be classified into either core disgust, animal-reminder
disgust, interpersonal disgust, or moral disgust (Rozin et al., 2008). Alternatively, some
researchers clustered these factors into two broad domains, one signifying “core” or “primary
disgust” and the other signifying “complex” or “socio-moral disgust” (Haidt et al., 1997; Izard,
1993; Leathers-Smith & Davey, 2011; S. W. S. Lee & Ellsworth, 2013; Marzillier & Davey, 2004;
Rozin, Haidt, McCauley, et al., 1999). Previous psychometric research has suggested that, while
there is overlap, animal-reminder disgust can be considered distinct from core disgust (Olatunji et
6
al., 2008). We further discuss the three distinct domains, (core, animal-reminder, and socio-moral)
from this broad classification of disgust below.
Figure 1. Conceptual model of the overlap between the different domains of disgust
7
Table 1. Description of the different domains in our conceptual model of disgust
Domain of disgust Description Examples
Core
• Includes stimuli that traditionally elicit physical revulsion
• Stimuli or acts elicit disgust because of their potentially
contaminating or nauseating nature
• Feces
• Placenta
Animal-reminder
• Elicitors MUST be common between humans and animals
• Felt disgust is rooted in feelings of how similar the animal
"act" is to oneself or humans, in general
• Overlaps with core disgust elicitors, but differs in the nature
of felt disgust
• Defecation
• Birthing
• Sexual acts
• Body-envelope
violations
Sociomoral
• Includes stimuli that pose a threat to one's own personal
space or conscience
• Disgust elicits feelings of strangeness, threat of
contamination, or immorality/taboo
• While core disgust is elicited by objects or animals, social
disgust is caused by uniquely human acts and social cues
• Avoiding
interaction with
strangers
• Avoiding a
maskless
individual
• Incestuous
relationships
Complex
• There is an influence of cultural upbringing on disgust
feelings
• Certain acts or stimuli may elicit a complex mix of disgusted
feelings
• Includes a mix of feelings of physical revulsion, being
reminded of the animalistic nature of the act, threat of
contamination or impurity from that person
• Since sociomoral disgust is considered uniquely human,
complex disgust elicitors would be limited to human-related
acts or stimuli.
• Public
breastfeeding
1.3.1. Core disgust
Core or physical disgust includes stimuli such as disgusting foods (eg. seeing moldy bread,
smell of sour milk), bodily wastes (eg. dog feces, fudge piece that looks like excrement) and
animals (eg. mucous-covered snails, rats going through garbage) (Olatunji & Sawchuk, 2005;
Rozin & Fallon, 1987; Vicario, Rafal, Martino, et al., 2017). In order for a stimuli to be considered
a core disgust elicitor, it must satisfy the following conditions: (a) pose a perceived or real threat
of potential ingestion (sense of incorporation), (b) cause an aversive reaction or be offending (sense
of offensiveness), and (c) have been evaluated as a contaminant (contamination potency), which
8
can be shaped by one’s cultural environment (Angyal, 1941; Rozin et al., 2008; Rozin & Fallon,
1987). Many stimuli that engender core disgust feelings are commonly considered contaminants
(eg. vomit, mucous) and tend to cause similar behavioral reactions within (eg. prevention of direct
contact, use of tissues) and across most cultures (Azlan et al., 2017; Curtis et al., 2004; Ekman,
1992; Olatunji & Sawchuk, 2005).
1.3.2. Animal-reminder disgust
Animal-reminder disgust, or existential disgust (Giner-Sorolla et al., 2018), includes
elicitors such as sexual practices, personal grooming, bodily injury and death. It has been posited
that such stimuli are related to remind us of our own animalistic nature and mortality. Humans and
non-humans animals are mortal beings (Fleischman, 2014). Other commonalities between humans
and non-human animals are the growth of hair, sexual behavior, pregnancy and breastfeeding
(Giner-Sorolla et al., 2018). According to Rozin and colleagues (2008) this domain of stimuli is
characterized by the existential fear engendered through the constant reminder of one’s eventual
demise or similarity with animals. Such perceptions can be influenced by one’s community and
are culturally motivated (Haidt et al., 1994; Olatunji & Sawchuk, 2005). For example,
sociocultural ideals surrounding personal grooming in women and attitudes towards women’s
body hair may differ from culture to culture. Western culture beauty norms have often led to
negative attitudes, most closely related to feelings of disgust, being directed towards women with
unacceptable body hair (Tiggemann & Lewis, 2004). While it is informed by morality and
sociocultural norms, unlike bodily moral disgust (eg. masturbation), animal-reminder disgust
sensitivity is associated with acts that are both common to humans and non-human animals (GinerSorolla et al., 2018). Reaction to such stimuli appears to be more of a defense mechanism, often
9
resulting in the development of cultural or sociomoral norms to maintain the idea that humans and
non-human animals are distinct and need to stay that way (Haidt et al., 1994). Such feelings of
animal-reminder disgust, once elicited, reinforce associated emotional feelings towards animals
and how different they are from humans (Goldenberg et al., 2001; Rozin et al., 2008).
1.3.3. Socio-moral disgust
It has been posited that negative evaluations of social norm violations can lead to feelings
of “impurity” which influences aversive behaviors directed towards immoral agents, which have
been deemed as “bad and harmful” in the same way physical disgust deems smelly food “bad”
(Curtis & Biran, 2001; Hedblom, 2019; Rozin, Haidt, & McCauley, 1999). Interpersonal disgust
elicitors include stimuli or situations that pose a threat to the safety of one's conscience, integrity
or personal space (Power & Dalgleish, 2015; Rozin et al., 1994). Interpersonal disgust elicitors
can be further classified into stimuli that elicit feelings of strangeness (eg., unwanted interactions
with strangers), moral taint (eg., feelings directed towards hypocrites and undignified individuals),
disease threat (eg, avoidant behavior around a person with AIDS), and misfortune or disfigurement
(eg., repulsion towards amputees) (Rozin et al., 1994; Rozin, Haidt, &-McCauley, 1999; Rozin,
Haidt, McCauley, et al., 1999).
Morality has been defined as the code of conduct proposed by all rational beings for
specific conditions (Gert & Gert, 2020). Haidt (2001) suggested that children have the innate
ability to internalize moral feelings across five foundational social domains that have evolved over
time to become intrinsically moral for humans (i.e., respecting authority, caring for the harmed,
being fair and being treated fairly, loyalty to the one’s “group”, and sanctity and purity). Moral
disgust elicitors are those stimuli that trigger feelings of threat against the social order and are
10
largely influenced by cultural environments, religious organizations, and/or legal institutions
(Olatunji & Sawchuk, 2005; Vicario, Rafal, Martino, et al., 2017). This form of disgust is uniquely
human and is triggered by “impure” or “harmful” social and moral stimuli (Rozin et al., 2008;
Schnall, Benton, et al., 2008). Moral disgust can be culturally biased (eg. caste system in India)
(Appadurai, 1981; Cohen et al., 2007; Danovitch & Bloom, 2009; S. W. S. Lee & Ellsworth, 2013;
Rozin, Haidt, & McCauley, 1999), or developed through generalizations made from interpersonal
or core domains to any offensive circumstance (eg., personal hygiene and ambulance chasers)
(Olatunji & Sawchuk, 2005; Rozin, Haidt, & McCauley, 1999). Although they are different
domains, interpersonal disgust and socio-moral disgust may have some overlap (Chapman &
Anderson, 2014; Danovitch & Bloom, 2009; Olatunji & Sawchuk, 2005; Rozin et al., 2008).
Nevertheless, the distinction between core, animal-reminder and sociomoral disgust lies in the
situations in which they occur and the behavioral signatures elicited (Kupfer, 2018; Shenhav &
Mendes, 2014). One contemporary talking point that reveals the overlap of core, animal-reminder,
and moral disgust feelings is public breastfeeding. While it is a perfectly healthy behavior, public
breastfeeding has received varying degrees of negative attitudes, especially within Western
cultures (Brown, 2018). Feelings of disgust or contempt for this act can be traced back to: 1)
disgust elicited by the act of revealing the breast, in defiance of sociocultural norms that perceive
the breast or its revelation as taboo for any reason and/or sexual in nature (Acker, 2009; Brown,
2018); 2) disgust based on similarities of the act of breastfeeding in humans with that of animals
suckling their young (Capponi & Roland, 2021); 3) the generalization that all bodily secretions
can be disease-causing or harmful, therefore perceiving breast milk as a potential contaminant
(Brown, 2018); or 4) all of the above.
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1.4. Disgust processing and disgust behaviors
1.4.1. Disgust proneness
Typical individuals within the same community may differ in their individual experience
of disgust (Inbar et al., 2009; Knowles et al., 2019; Olatunji, Puncochar, et al., 2016; Olatunji et
al., 2017; Olatunji & Sawchuk, 2005). Researchers believe that these individual experiences may
reflect a personality trait of disgust, called disgust proneness (Olatunji et al., 2017; Reynolds &
Askew, 2019; Sica et al., 2019; Viar-Paxton & Olatunji, 2016), further broken down into three
aspects - disgust propensity (van Overveld et al., 2006), disgust reactivity, and disgust sensitivity
(Viar-Paxton & Olatunji, 2016). Disgust propensity is defined as one’s predisposition to
experience disgust, while disgust reactivity is characterized by one’s predisposition to have a
disgust reaction on exposure to aversive stimuli (van Overveld et al., 2006; Viar-Paxton &
Olatunji, 2016). Finally, disgust sensitivity is defined as the degree of an individual’s negative
appraisal of a disgust experience (Olatunji & Cisler, 2009). Disgust sensitivity is context-specific
while disgust propensity and reactivity capture the trait-like qualities of disgust. Individual
differences in these aspects of disgust proneness could stem from an interaction of genetic factors
with environmental factors (eg. childhood socializing, family food preferences) (Olatunji et al.,
2017; Rozin & Millman, 1987; Sherlock et al., 2016; Stevenson et al., 2010). For example, higher
levels of disgust sensitivity and propensity have been associated with more conservative ideals,
which in the USA is commonly associated as a tendency to identify with and vote for the
Republican party (Shook et al., 2017). Prior studies indicate that correlations between
conservatism and disgust sensitivity remain even after controlling for racial prejudice (Inbar et al.,
2012; Shook et al., 2017; Tybur et al., 2010). While prejudicial behavior may be a product of one’s
environment, the link between conservative sociopolitical values and disgust sensitivity seems to
12
stem from a desire to protect one’s own purity (Graham et al., 2009). However, other researchers
have argued that this association between conservatism and disgust sensitivity is biased due to the
use of specific stimuli and disgust domains and argue instead that both conservatives and liberals
vary in their disgust proneness depending on the situation (Elad-Strenger et al., 2020). For
example, liberals, as compared to conservatives, may exhibit higher levels of disgust sensitivity
and show behaviors directed at protecting their purity, such as more negative attitudes towards
coal industries due to increasing environmental pollution (Elad-Strenger et al., 2020).
1.4.2. Vicarious disgust learning in children
Young children may not necessarily find stimuli disgusting, but still display disgust facial
responses to stimuli (Rozin & Fallon, 1987; Siegal & Share, 1990). One explanation for this
behavior may be the vicarious learning of disgust (Askew et al., 2014). Previous research has also
found that food preferences, and disgust responses by extension, in newborn children can be biased
through maternal diet (eg. garlic flavored foods) during pregnancy and breastfeeding (Hepper et
al., 2013; Mennella et al., 2001; Wagner et al., 2019). Additionally, olfactory sensory preferences
can be engendered in neonates through exposure into an their olfactory environment and
habituating them to novel odors (food-related or social odors) during early development (Schaal
et al., 2020). Thus, in the case of newborns whose behavioral responses are not biased by maternal
diet and olfactory environment, certain unfamiliar and unpleasant novel stimuli may elicit aversion
responses (Wagner et al., 2019). Such avoidance of certain foods during the development may not
only influence the sensory preferences of newborns, but also behavioral responses through
vicarious observation of maternal avoidance of foods and odors.
13
The facial expression of disgust is characterized by furrowed eyebrows, closed eyes or
constricted pupils, a wrinkled nose, a retracted upper lip and the “upward movement of the lower
lip and chin, and drawing the corners of the mouth down and back” (Olatunji & Sawchuk, 2005,
p. 936), and it is universal (Ekman et al., 2013). Research has shown that, much like vicarious
learning of fear, disgust responses can be learned by observation in children older than seven or
eight years, and both fear and disgust maintain a bidirectional learning pathway (Askew et al.,
2014). Although, unlike fear responses, disgust responses are not necessarily rapid (Knowles et
al., 2019), perhaps due to the fact that while disgust stimuli pose a threat of contamination, fear
stimuli represent imminent or potential bodily harm and thus require more rapid learning
(Armstrong & Olatunji, 2017). More recently, Reynolds and Askew (2019) provided the first
evidence of the development of attentional biases to perceived threat through vicarious learning of
disgust in children (mean age = 8.25 years). They found increased responding and more attentional
biases to disgusting stimuli following vicarious disgust learning was similar to increases and biases
seen in fear responding after vicarious fear learning. From an evolutionary perspective, this ability
to learn about stimuli vicariously from another’s experience of disgust or fear could have evolved
preferentially in humans, allowing them to learn without experiencing the same potentially harmful
or lethal stimulus (Reynolds & Askew, 2019). Child age is an important factor limiting disgust
learning and its importance may be explained by either predominant maternal supervision
protecting the child during development (Olatunji & Sawchuk, 2005), problems with cognitive
evaluation of the disgust facial expression (eg. confused with another negative emotion (Widen &
Russell, 2013), or lack of understanding of complex concepts like nausea or cultural ideations
(Rozin et al., 2008; Rozin & Fallon, 1987).
14
1.4.3. Disgust-related behavioral responses
The most common reaction to the experience of disgust is avoidant behavior to protect the
individual from aversive stimuli (Izard, 1993). This avoidant behavior can be either active, passive,
or have elements of both. Active avoidance involves an actual physical escape from the aversive
stimulus upon exposure and can lead to the development of preemptive avoidant behaviors of
situations that may include the disgust threat through repeated exposures to aversive stimuli.
Passive avoidance involves non-active escape or rejection of an already exposed aversive stimulus
(e.g., pushing disgusting food away) (Olatunji & Sawchuk, 2005). Studies have shown that
individuals generally have a higher tendency to behave passively rather than escape in response to
disgusting stimuli (Rozin et al., 2008). These behavioral tendencies are strongly influenced by the
social and cultural environment, rather than just our evolutionary predisposition to avoid aversive
stimuli (Haidt et al., 1997; Rozin & Millman, 1987; Stevenson et al., 2010).
Aside from avoidant behavior, there are physiological and psychological responses that are
elicited by disgust-causing stimuli. Previous older research has documented the different forms of
physiological responses to disgust stimuli moderated by the parasympathetic nervous systems
(Levenson, 1992). Parasympathetic responses included slower heart rates (Levenson et al., 1990;
Page, 1994), reduced blood pressure (Sledge, 1978), slower respiration rate (Curtis & Thyer,
1983), and lower skin temperature (Zajonc & McIntosh, 1992). Other autonomic responses include
higher levels of salivation (Carlson, 2012; van Overveld et al., 2008), reduced gastric activity
(Shenhav & Mendes, 2014), and gastrointestinal (GI) movements preceding nausea and vomiting
(Ekman et al., 1983). However, unlike core disgust elicitors, researchers have found that moral
disgust is followed by sympathetic responses, including elevated heart rate, reduced vagal tone,
increased breathing and other imbalanced autonomic responses (Alaoui-Ismaïli et al., 1997;
15
Ottaviani et al., 2013; van Overveld et al., 2009; Vernet-Maury et al., 1999). This behavioral
response may be more similar to feeling anger rather than disgust (S. W. S. Lee & Ellsworth, 2013;
Levenson, 1992; Ottaviani et al., 2013). However, the literature on autonomic responses to disgust
is both limited and inconsistent (Scott, 2019). A recent study of heart rate variability during felt
disgust and anger towards moral violations revealed conflicting evidence for heart rate during felt
disgust, i.e. they observed a reduction in heart rate for moral disgust, while increased heart rate
was associated with anger (Konishi et al., 2020). Given the contrast in the evidence, an argument
can be made that the variability in heart rate patterns could be influenced by mixed individual
feelings of both anger and disgust and that different types of stimuli may elicit varying levels of
mixed feelings from different individuals (Scott, 2019).
Another possibility is that the feelings of contempt often associated with evaluations of
morally “impure” stimuli is a derivative of both anger and disgust emotions (Power & Dalgleish,
2015), thus explaining the sympathetic responses towards moral disgust elicitors. Developmentally
speaking, researchers have found that learning of physical or core disgust occurs within the toddler
phase of life (Bloom, 2009) and precedes the development of moral disgust in children (Danovitch
& Bloom, 2009). The variability in moral disgust responses in later life is highly influenced by the
social and cultural norms of the surrounding environment, as well as individual differences in
perspective and understanding of morality (Cohen et al., 2007; Haidt et al., 1997; S. W. S. Lee &
Ellsworth, 2013; Rozin, Haidt, McCauley, et al., 1999).
1.4.4. Disgust and Cognitive Bias
Disgust has become increasingly important in psychology research because of the strong
association between levels of disgust proneness with anxiety, obsessive-compulsive disorder
16
(OCD) and depression-related psychopathology (Alanazi et al., 2018; Knowles et al., 2019;
Olatunji et al., 2017; Olatunji & Cisler, 2009). Disgust elicitors also can cause negative selfappraisal because of one’s reactivity and sensitivity towards stimuli, thus leading to associated
increases in self-disgust levels (Azlan et al., 2017; Beard et al., 2017; Gilbert, 2015; Olatunji &
Sawchuk, 2005). Excessive disgust processing has been found to generate different types of
cognitive biases, similar to those found in anxiety disorders (Davey et al., 2006; Leathers-Smith
& Davey, 2011; Mayer et al., 2009; Whitton et al., 2013). A recent review (Knowles et al., 2019)
of cognitive biases affecting disgust processing identified certain key types of biases - memory
bias (Muris & Field, 2008), interpretation bias (Beard et al., 2017; Hirsch et al., 2016), expectancy
bias (Davey, 1995; Foa et al., 1996; Rachman, 1990), orientating and attentional bias (Armstrong
et al., 2012; Cisler & Koster, 2010; Reynolds & Askew, 2019; Zhao et al., 2016). These cognitive
biases can lead to maladaptive disgust processing, like exclusive focus on negative aspects of
experiences and biased memory recall associated with the disgust stimulus (Beck, 2008).
Researchers have documented that these cognitive biases may be predictors of potential
psychopathology, especially in children, by causing excessive disgust processing and attentional
focus on disgusting stimuli (Muris et al., 2008; Muris & Field, 2008; Olatunji et al., 2010; Whitton
et al., 2013). Development of such cognitive biases also reinforces avoidant behaviors towards
potentially beneficial medical procedures that include exposure to perceived disgust threat (Azlan
et al., 2017; Reynolds, Consedine, Pizarro, & Bissett, 2013) and enables generalizations of disgust
and contamination threat to ambiguous stimuli and situations which further limits individual
emotional flexibility (Beard et al., 2017; Lawson et al., 2002; Leathers-Smith & Davey, 2011).
However, current literature reveals mixed results in the identification of generalized patterns of
cognitive biases and how affected disgust processing confers psychopathology (Knowles et al.,
17
2019). One likely explanation is individual differences in learned experiences and the different
aspects of disgust proneness - sensitivity, propensity and reactivity (Knowles et al., 2019; Olatunji
& Sawchuk, 2005; van Overveld et al., 2006). Another proposal for this variability is the combined
cognitive bias hypothesis (Hirsch et al., 2006) that postulates that at different levels of information
processing, a cognitive bias at one level may influence the development of a bias at another (eg.
attentional bias changing one’s interpretation, memory bias focusing one’s attention) (Everaert et
al., 2014). Unfortunately, while moral disgust does influence behavior (Nichols, 2002; Olatunji,
Ebesutani, et al., 2016) and has the tendency to result in the dehumanization of different social or
racial groups (Hedblom, 2019), there is yet insufficient evidence to show the effect of moral
disgust stimuli on cognitive biases and further research is warranted (Knowles et al., 2019).
1.4.4. Disgust and Moral processing
Another dimension of disgust-related behaviors is its influence on moral evaluations.
Researchers have found that the perception of a physically disgusting stimulus can bias the moral
judgment during evaluations of moral actors (Schnall, Benton, et al., 2008; Tracy et al., 2019;
Wicker et al., 2003). These findings may stem from the fact that core disgust and sociomoral
disgust both induce the same oral-nasal rejection behavioral and facial responses (Chapman et al.,
2009; Chapman & Anderson, 2012; Ekman et al., 2013). Cannon and colleagues (2011) further
investigated the association of the disgust facial response and different moral foundations, finding
the disgust facial response was most associated with moral transgressions involving violations of
purity and fairness, while anger facial responses were more associated with harm violations .
Additionally, they found that the intensity of the facial response corresponded with the perceived
severity of the moral violations (Cannon et al., 2011).
18
The relationship between core and sociomoral disgust and the association of the disgust
response with morally disgusting situations also has been tested and revealed to be true in young
children (Danovitch & Bloom, 2009). Conversely, feelings of core and sociomoral disgust may be
manipulated using olfactory stimuli that either inhibit or elicit nausea (Royet et al., 2001; Schnall,
Haidt, et al., 2008; Tracy et al., 2019; Wicker et al., 2003). Wicker and colleagues (2003) used
nauseating stimuli during neuroimaging to identify a common mechanism for disgust-related selfexperiences and vicarious understanding of disgust. Wheatley and Haidt (2005) found that the
sensation of an extrinsic disgusting odor can bias moral evaluations to be harsher than controls.
Expanding on this relationship, Schnall and colleagues (2008) conducted a series of experiments
to test the effect of extrinsic disgusting stimuli on moral evaluations. They found that the
perception of a disgusting odor, presence within a disgusting room, watching disgusting videos,
or vividly remembering a disgusting experience can influence harsher moral evaluations.
However, the influence of disgust on moral judgment when in a disgusting room or remembering
touching something disgusting was only true in participants that revealed higher levels of
interoceptive awareness (Schnall, Haidt, et al., 2008). Tracy and colleagues (2019) used an antinauseating stimuli (antiemetic ginger) to show how inhibited feelings of disgust can result in lower
feelings of disgust towards non-moral and morally disgusting stimuli and less harsh moral
judgments, even in those individuals with high disgust sensitivity with a tendency for harsher
judgments.
Disgusting smells can also influence feelings of interpersonal trust, which could undermine
social trust and relationships and bias moral evaluations of others. For instance, the smell of rotten
or decaying organic matter is often used as a metaphor for food that might be “suspicious” in its
quality or food viability, with the food material usually described as “fishy” (Lakoff & Johnson,
19
2008; Lee & Schwarz, 2012). Lee and colleagues (2015) further investigated this analogy and
concluded that priming of participants with a fishy odor influenced feelings of mistrust in
information, evidenced by higher incidences of distorted information detection but no significant
differences in the detection of undistorted information. Thus, disgusting smells may influence
moral judgment via both direct and indirect routes.
1.5. Neuroimaging studies
In the late 1990s, when compared to other neural research on emotions, disgust research
remained fairly understudied (Vrana, 1993). Early stimulation studies also revealed the role of the
insula as a hub for nauseated behaviors (Penfield & Faulk, 1955). Initial lesion studies on the
neural correlates of all six basic emotions (fear, anger, sadness, disgust, joy, surprise; Ekman,
1992) found that reduced emotion information processing was associated with damage in the
bilateral amygdala, cingulate cortex, insula, supramarginal gyrus and medial prefrontal cortex
(Adolphs et al., 1999; Dal Monte et al., 2013). In particular, damage in the insula was associated
with difficulty recognizing the disgust emotion, indiscriminate food consumption and the absence
of contaminant-avoiding behavior (Adolphs et al., 2003). Unfortunately, disgust-centered
neuroimaging studies faced challenges in delineating the correlates of disgust from those of other
negative basic emotions because of the shared neural circuitry (amygdala, hippocampus,
orbitofrontal cortex, basal ganglia, and occipitotemporal cortices) involved in processing these
emotions (Adolphs, 2002; Olatunji & Sawchuk, 2005). Most notably, Adolphs (2002) proposed
that while fear-related processing may activate the amygdala more, disgust-related processing may
be different due to preferential activations in the insula and basal ganglia. More recently,
researchers have further investigated the neurobiological and neural underpinnings of disgust
20
(Vicario, Rafal, Martino, et al., 2017). Emerging evidence has revealed that disgust is multi-faceted
and individual differences in disgust proneness, socio-cultural factors and educational
environments are important influential factors on the neural activity associated with disgust
(Curtis, 2011; Davey, 2011; Vicario, Rafal, Borgomaneri, et al., 2017).
1.5.1. Insula
Research has suggested that different divisions of the insular cortex (dorsal anterior, ventral
anterior and posterior regions) may be involved in different functions (Chang et al., 2013; Craig,
2009). For instance, the anterior insula (AI) has been associated with interoception and may be
responsible for homeostatic responses to hunger, resulting in food-seeking behaviors (Barrett &
Simmons, 2015; Craig, 2002, 2009). Chang and colleagues (2013) meta-analytically decoded the
functions of different network divisions of insula and found distinct functional priorities for the
dorsal anterior, ventral anterior and posterior divisions. The dorsal anterior insula (dAI) was
revealed to be selectively involved in response inhibition, executive functioning, task switching,
error processing, and other goal-directed cognitive tasks, while the ventral anterior insula (vAI)
was more involved in autonomic functioning, chemosensory processing (olfaction, gustation) and
emotion processing (Chang et al., 2013; S. M. Nelson et al., 2010). The posterior insula (PI) was
found to be a multimodal hub of various sensory information and was involved in the processing
of exteroceptive (external sensations) and interoceptive (internal sensations) signals (Cauda et al.,
2012; Chang et al., 2013). The collective findings of AI and PI activity during interoception
(Barrett & Simmons, 2015; Chang et al., 2013; Craig, 2002, 2009) also supports Craig’s
hypothetical model of how information related to interoceptive awareness and the self is integrated
along a posterior-to-anterior trajectory within the insula (Craig, 2009).
21
Early neuroimaging and clinical studies on emotion and disgust initially implicated the
insula in the information processing of core and social disgust stimuli (Calder et al., 2000, 2001,
2007). Further research consolidated the role of the AI and associated networks in processing core
disgust stimuli and disgusted facial expressions (Borg et al., 2013; Fusar-Poli et al., 2009; Murphy
et al., 2003), as well as moral disgust processing (Chapman et al., 2009; Sanfey et al., 2003). Other
studies have reported distinct activation patterns in the AI associated with the vicarious, mentalized
and experienced disgust processing (Jabbi et al., 2008). Since disgust is also a visceral experience,
recent findings have suggested a network of connections between the insula and frontotemporal
regions involved in processing interoceptive, emotional and socio-cognitive information (Adolfi
et al., 2017). One review of disgust-related neurobiological correlates advocated for the view of
the AI as an allostatic hub region that manages inputs from the insula-related networks (striatum,
putamen, medial prefrontal, orbitofrontal, sensory, and anterior cingulate cortices), which
potentially process specific information associated with domain-specific disgust experiences
(Vicario, Rafal, Martino, et al., 2017). Interestingly, the particular neural mechanisms underlying
domain-specific disgust processing may overlap partially with each other, such as activations of
the same regions in insula and anterior cingulate cortices (ACC) for both core and social disgust
experiences (Jabbi et al., 2008; Vicario, Rafal, Martino, et al., 2017; Wicker et al., 2003).
However, some researchers have found that the insula, as well as the amygdala, are no
more active during experiences of disgust than they are during fear (Barrett et al., 2006; Schäfer
et al., 2005; Schaich Borg et al., 2008; Stark et al., 2003). On the other hand, more recent studies
have provided evidence that emotion processing in the insula may not be disgust-specific, but
rather involved in the general processing of both positive and negative emotions (Cauda et al.,
2012; Menon & Uddin, 2010). Some researchers also have suggested that insula network-related
22
regions such as prefrontal and postcentral cortices, the putamen, thalamus, and mammillary bodies
are responsible for the processing of negative emotional and social information, and not the insula
(Couto et al., 2013; L. A. J. Kirby & Robinson, 2017).
Possible explanations for these contradictory findings might be the role the insula plays in
general emotion processing (Barrett et al., 2006), in language processing during text-based disgust
scenario tasks (Nestor et al., 2003; Ogar et al., 2006), or in processing visceral state information
during conscious experience of felt emotions (Damasio, 2003; Damasio et al., 2000). Given these
findings, an investigation into the effective connectivity of the insula with its afferent nodes and
the influence of socio-cultural and environmental factors on disgust domains could further
illustrate how individual differences influence disgust proneness and risk of psychopathology.
1.5.2. Social disgust
Apart from the anterior insula, there also may be other major regions in the brain associated
with social disgust processing, such as regions associated with facial expression processing
(Vicario, Rafal, Borgomaneri, et al., 2017). Social disgust is characterized by the ability to
recognize another person’s disgusted facial expression and vicariously experience feelings of
disgust (Askew et al., 2014; Reynolds & Askew, 2019). This can be seen in the involvement of
brain regions such as inferior occipital regions, fusiform face area (FFA) and the posterior superior
temporal sulcus (pSTS) in the disgusted face processing, activating alongside limbic regions like
the amygdala (Haxby et al., 2000; Hubl et al., 2003).
23
1.5.3. Disgust Mirroring
As previously mentioned, core and social disgust elicitors tend to activate the same regions
in the AI and ACC (Jabbi et al., 2008; Wicker et al., 2003), thus suggesting an underlying mirroring
phenomenon of observed disgust (Keysers & Gazzola, 2009; Rizzolatti & Sinigaglia, 2016;
Wicker et al., 2003). Vicario and colleagues (2017) recently investigated this phenomenon and
found that individual differences in disgust sensitivity predicted the suppression of tongue motor
activity (cortico-hypoglossal inhibition) upon viewing disgusting stimuli and disgusted faces.
They hypothesized that the inhibition upon viewing disgusting foods represents individual biased
responses upon seeing a potential disgust threat, while tongue inactivity to disgusted faces
represents internal associations between personal somato-gustatory experiences and the observed
faces resulting in the motor action mirroring phenomenon (Vicario, Rafal, Borgomaneri, et al.,
2017).
1.5.4. Moral disgust
The “social intuitionist theory” (Haidt, 2001) suggests that children have the innate ability
to internalize moral feelings across five foundational social domains that have evolved over time
to become intrinsically moral for humans (i.e., respecting authority, caring for the harmed, being
fair and being treated fairly, loyalty to the one’s “group”, and sanctity and purity). Additionally,
this theory accounts for the cultural variability in moral values by suggesting that one’s moral code
is influenced by the social domains prioritized within one’s culture (Graham et al., 2009; Haidt et
al., 1997; Rozin et al., 2008), thus accounting for individual differences in moral reasoning.
Negative evaluations of social norm violations can lead to feelings of “impurity” which influences
aversive behaviors directed towards immoral agents, which have been deemed as “bad and
24
harmful” in the same way physical disgust deems smelly food “bad” (Curtis & Biran, 2001;
Hedblom, 2019; Rozin, Lowery, Imada, & Haidt, 1999). Moral foundations theory (Graham et al.,
2013), an extension of social intuitionist theory, redefines these moral intuitions into moral
foundations that were developed to resolve social problems. According to these researchers, there
are six moral foundations that predispose individuals to dislike certain behaviors - Care/Harm,
Fairness/Cheating, Loyalty/Betrayal, Authority/Subversion, Sanctity/Degradation,
Liberty/Oppression (Graham et al., 2013; Haidt, 2012; Iyer et al., 2012). Care violations included
acts that involved emotional harm or physical harm towards either human or non-human beings.
Fairness violations included acts of cheating (eg. on an exam) or free-riding (eg. dishonest work
hours). Loyalty violations included selfish acts for individual benefit over the benefit of the
“group”, defined as any family, team, country, company, school, etc. Authority violations are acts
involving the disobedience or disrespect of an authority figure (eg. parent, boss) or
institution/symbol (eg. the National anthem, government). Sanctity violations include degrading
acts (eg. drunkenly groping someone), sexually deviant acts or acts that pose a risk of
contamination (eg. urinating in a public pool). Liberty violations include acts of coercion or acts
that limit individual freedom or choices (eg. voting restrictions), usually by figures in a position
of power (Clifford et al., 2015; Graham et al., 2013; Haidt, 2012). Of these moral foundations,
sanctity violations have been most closely linked with the emotion of disgust (Horberg, Oveis,
Keltner, & Cohen, 2009; Horberg, Oveis, & Keltner, 2011), including associations between
responses with the disgust facial expression (Ekman et al., 2013) and sanctity (or purity) violations
(Cannon et al., 2011). However, some researchers suggest that disgust is more likely emotionally
linked with all the moral foundations (Eskine et al., 2011; Schnall, Haidt, et al., 2008; Tracy et al.,
2019; Wheatley & Haidt, 2005).
25
As morality and disgust are linked, many of the brain regions associated with moral
reasoning and decision-making have been found to be active during the processing of morally
disgusting situations (for a review, see Pascual, Gallardo-Pujol, & Rodrigues, 2013). However,
many of the studies investigating moral disgust have done so within only one or two specific
contexts, such as assessing moral reasoning of adults towards incestuous or sexually
immoral/amoral situations (Ottaviani et al., 2013; Schaich Borg et al., 2008). Current
neuroimaging literature on moral disgust follows the view that morality as a concept not
represented by one system within the brain, but rather recruits different regions based on the moral
context and type of transgression (Ottaviani et al., 2013; Parkinson et al., 2011; Pascual et al.,
2013; Schaich Borg et al., 2008). Thus, depending on the moral transgression being studied,
different regions may show variable levels of activity.
While such variability makes it difficult to identify a unique neural signature for moral
disgust, some research has shown that moral and physical disgust situations may be evaluated by
different regions in the brain and shows distinct patterns of activity (Greene & Paxton, 2009; Moll
et al., 2005; Schaich Borg et al., 2008). Parkinson and colleagues (2011) found that while
disgusting situations elicited activity within affective processing regions, the assessment of
harmful and dishonest situations elicited activity within action understanding and mentalizing
regions respectively. Significant activity associated with core disgust processing was identified
within the bilateral amygdala, AI, right ACC, fusiform gyrus, posterior precuneus and temporal
poles. Dishonest situations, which participants commonly view as “morally disgusting”, were
marked with significantly increased activity in the left dorsolateral prefrontal cortex (dlPFC) and
the bilateral temporoparietal junction (TPJ). In comparison, harmful situations elicited significant
activity within the supplementary motor area (SMA), inferior parietal lobule and the anterior
26
precuneus. The only common region associated with general disgust domain-specific processing
was the dorsomedial prefrontal cortex (dmPFC) which showed consistently higher activity during
each of the conditions (Parkinson et al., 2011). More recently, neuroimaging researchers have
found significant associations of moral disgust processing with functional connectivity within the
TPJ (Lim et al., 2017) and significant activity with the left nucleus accumbens (NAcc) in patients
with OCD (Fontenelle et al., 2018). Multiple lesion and neuroimaging studies also have identified
that the anterior insula (Belfi et al., 2015; Chau et al., 2018; Fumagalli & Priori, 2012; Greening
et al., 2014; King-Casas et al., 2008; Shoemaker, 2012), cingulate cortices (both ACC and PCC)
(Fumagalli & Priori, 2012; Shoemaker, 2012) and the ventromedial prefrontal cortex (vmPFC) (S.
W. Anderson et al., 1999; Cameron et al., 2018; Fumagalli & Priori, 2012; Moretto et al., 2010)
play key roles in morality processing and the processing of emotions related to moral situations
and damage to these areas can result in difficulties with disgust emotion and social stimuli
processing (insula, PCC), the understanding of social norms, moral norms and intentions (insula,
vmPFC), balancing rational and moral decision making (ACC), and social trust (insula). Thus,
clinically speaking, reduced functional integrity in any of these brain regions can result in
difficulties in understanding moral situations and making appropriate moral judgments.
27
Figure 2. Conceptual model of the relationship between disgust, sensory and moral processing
regions in the brain and the hypothesized effect of Autism Spectrum disorder (ASD)
symptomatology on these functional connections. Functional links are shown in black arrows
28
with triangular tips, somatic state linkage is shown with a red arrow with diamond tips, and the
effect of ASD symptoms are shown in orange dashed arrows with rounded tips. ACC, anterior
cingulate cortex; VMPFC, ventromedial prefrontal cortex; DLPFC, dorsolateral prefrontal
cortex; PCC, posterior cingulate cortex; SMA, supplementary motor area. Adapted from somatic
marker hypothesis models in Bechara, 2013; Koob, Arends, McCracken, & Le Moal, 2019;
Saive, Royet, & Plailly, 2014.
A recurring theme in current disgust processing research is how individual differences in
disgust predispositions, personality, socio-cultural upbringing, educational environments, and
genetic-environment interactions influence the reactions to disgust experiences and the neural
activation patterns (Borg et al., 2013; Borgomaneri et al., 2015; Dapretto et al., 2006; Vicario,
Rafal, Borgomaneri, et al., 2017). Future research on disgust proneness must account for this
variability by measuring these influential individual factors and including them in analyses of
neural activations.
1.6. Disgust experiences of the pediatric ASD population
ASD symptomatology is characterized by social interaction and communication
impairments, repetitive and restricted behaviors, challenges with sensory processing and proper
GI functioning (Lord, 2010; Marco et al., 2011; Ristori et al., 2019). Although prior work on
disgust processing in ASD has been limited, two noticeable pathways by which disgust reacts with
ASD symptoms can be seen in available literature - diminished contamination-related behaviors
(eg. pica) (Kalyva et al., 2010; Siegal et al., 2011) and deficits in attentional bias and emotional
face processing (Katarzyna et al., 2010; Monk et al., 2010; Xu et al., 2015; Yeung et al., 2020).
Additionally, individuals with ASD are known to process information regarding moral scenarios
differently (Pascual et al., 2013) and to render more punishing decisions based on the outcome of
scenarios rather than intents of agents (for a review, see Dempsey et al., 2020). Given the close
29
relationship between morality and disgust and its potential to influence social behavior (Hedblom,
2019; Power & Dalgleish, 2015) and relationships (Howlin et al., 2013; Kasari et al., 2011;
Rakoczy et al., 2016), some researchers have investigated the association between ASD
symptomatology and moral disgust-based decision making (Dempsey et al., 2020; K. Gray et al.,
2012; Li et al., 2014; Margoni et al., 2019; Margoni & Surian, 2016). In this section, we will review
the current literature in each of the following domains - core disgust, facial processing of disgust,
and sociomoral disgust.
1.6.1. Core disgust in ASD
Children with ASD are known to be more likely at risk of eating items that were not meant
for consumption (Matson et al., 2009). One of the ways children generally learn about food and
contamination-related behaviors during development is social learning and communication
(Rozin, 1990; Rozin & Millman, 1987; Siegal, 2008). Typically-developing toddlers often learn
through detecting socio-communicative signals early in their development which seems to help
reinforce contamination-related behaviors (Kalyva et al., 2010; Oaten et al., 2014; Senju & Csibra,
2008). Learning through social communication is considerably challenging for children with ASD,
who have difficulty attending to speech and vocal signals (Russo et al., 2009; Watson et al., 2003).
Another potential source for difficulties with appropriate disgust learning in children with ASD is
their reduced sensitivity to emotions, especially negative emotions (Losh & Capps, 2006; Uljarevic
& Hamilton, 2013). Thus, children with ASD may not attend to conversations with their caregivers
on topics of food, edibility and contamination, unlike typically-developing children that are more
predisposed to such social learning (Kalyva et al., 2010). Neuroimaging evidence suggests that
dysfunctional integrity and connectivity within the AI can result in divergent disgust experiences,
30
both general disgust and vicarious disgust (Shoemaker, 2012). Furthermore, studies investigating
the functional integrity of the insula in individuals with ASD have found consistent findings of AI
dysfunction resulting in difficulties with emotion, empathic and social processing (Anderson,
Druzgal, et al., 2011; Anderson, Nielsen, et al., 2011; Bird et al., 2010; Caria & de Falco, 2015;
Ebisch et al., 2011). Hence, atypical functional connectivity in the AI in children with ASD may
underlie their difficulties with disgust emotion processing. There is also increasing evidence that
children with ASD suffer from GI symptoms (Erickson et al., 2005; Mayer, Knight, Mazmanian,
Cryan, & Tillisch, 2014). Given the role of disgust processing in avoiding disease-causing stimuli
(Oaten et al., 2009), it is possible that disgust processing disturbances may exacerbate the prevalent
GI problems in ASD (Kalyva et al., 2010; Kral et al., 2013; Ristori et al., 2019), or that dysbiosis
in the GI tract may contribute to problems with disgust processing (Pace-Schott et al., 2019).
1.6.2. Facial recognition of disgust in ASD
Since the processing of facial emotion expressions is involved of transmission of disgust
experiences and responses (Rozin & Fallon, 1987; Tompkins, 1963; Uljarevic & Hamilton, 2013;
Vicario, Rafal, Martino, et al., 2017), another dimension by which adequate disgust processing can
be limited in children with ASD is through their facial affect recognition deficits (Enticott et al.,
2014; Harms et al., 2010; Ozonoff et al., 1990). Many studies have found compelling evidence of
impairments to facial emotion recognition (FER) of negative emotions, especially disgust and
anger, in children with high-functioning ASD (Ashwin et al., 2007; Enticott et al., 2014; Harms et
al., 2010; Law Smith et al., 2010; Yeung et al., 2020). Studies on the neural correlates of FER and
face processing have revealed mixed results on the fusiform gyrus activations, reporting both
decreased (Hubl et al., 2003) and increased activity to familiar faces (Bookheimer et al., 2008;
31
Pierce & Redcay, 2008). Similar mixed findings of decreased (Dapretto et al., 2006; Pelphrey et
al., 2007) and increased (Monk et al., 2010; Piggot et al., 2004) patterns of activity have been
reported for the amygdala during FER. Decreased activity and connectivity in the inferior frontal
gyrus (IFG) also may explain impairments in the mirroring of facial affect (Dapretto et al., 2006;
Greimel et al., 2010; Nomi & Uddin, 2015b; Patriquin et al., 2016).
Additionally, FER has been associated with increased brain activity in individuals with
ASD compared to neurotypical counterparts within the ACC (Ashwin et al., 2007; Pelphrey et al.,
2007), precuneus (Wang et al., 2004), and anterior parietal and occipital cortices (Dapretto et al.,
2006). These findings could imply that individuals with ASD place more demand on attentional
and self-monitoring brain regions to actively decode facial affect (Harms et al., 2010). Recent
studies on attentional biases in children with ASD provide an explanation for the increased activity
in attention networks and the amygdala (Katarzyna et al., 2010; Xu et al., 2015; Zhao et al., 2016).
Zhao and colleagues (2016) found that children and adolescents with ASD exhibit increased
attention bias and hypervigilance on viewing faces with disgust, leading to persistent
disengagement and avoidant gaze behavior to any of the emotional faces. Persistent avoidance of
facial expressions could prevent neural habituation and processing of facial emotions, thereby
prolonging the FER difficulties (Kleinhans et al., 2009; Zhao et al., 2016). Oxytocin therapy is a
promising therapy for the social cognition deficits seen in individuals with ASD (Cochran et al.,
2013). A study found that oxytocin could only enhance facial recognition of happy and neutral
faces, but not negative emotion faces, indicating that the effect of oxytocin may be emotionspecific (Xu et al., 2015).
32
1.6.3. Sociomoral disgust in ASD
Early perspectives of moral development and decision-making in children with ASD were
based on rationalist theories of morality (Kohlberg, 1969, 1971; Piaget, 1965; Turiel, 1983) that
purported the importance of understanding the intentions of the moral agents (theory of mind
[ToM]/mentalizing), role-taking, recognition of the victim’s emotional state (emotional
intelligence) and empathy. Indeed, the literature on ASD symptomatology reveals many studies
investigating the mentalizing and empathic abilities of children with ASD (Baron-Cohen et al.,
1985; Bos & Stokes, 2019; Dziobek et al., 2008; Mazza et al., 2014; Patil et al., 2016; Rueda et
al., 2015). Children with ASD can often ascribe mental states to other people during ToM tasks,
yet show slower mentalizing ability when compared to neurotypical individuals (Baron-Cohen et
al., 1985; Senju et al., 2009). One group opined that individuals with ASD tend to have more
“immature” outcome-based moral reasoning compared to counterparts without problems with
mental state understanding and were capable of making “mature” intent-based moral reasoning
(Margoni & Surian, 2016). Another group corroborated this opinion by revealing less sophisticated
moral reasoning for transgressions of others during a Rule Transgression task (eg. lying at a job
interview) and harsher judgments for both intentional and unintentional actions during a Social
Intentionality task (eg. a confidant giving away embarrassing information purposely/by mistake)
in adults with ASD (Bellesi et al., 2018). Dempsey and colleagues (2020) argue that instead of
approaching the capacity for moral justifications from a deficits-based perspective, research has
shown individuals with ASD tend to make more concrete, outcome-based moral judgments over
intent-based moral judgments (Fadda et al., 2016; Zalla et al., 2011; Zalla & Leboyer, 2011), as
well as prioritizing outcomes over intents in their justifications (Dempsey et al., 2020).
Alternatively, Margoni and colleagues (2019) found that children with ASD were able to make
33
intent-based moral judgments and that previous differences could be explained by the prevalence
of executive dysfunction and difficulties inhibiting a prepotent outcome response (Bellesi et al.,
2018; Hill, 2004; Vyas et al., 2017). Also, individuals with ASD may require that the moral agent’s
intentions be explicitly described in order to make appropriate intent-based moral judgments
(Bellesi et al., 2018; Grant et al., 2005), while unclear or vague descriptions of intentions will
predispose them to making outcome-based moral decisions and more harshly punishing the
unintentional moral agents (Buon et al., 2013; Koster-Hale et al., 2012; Salvano-Pardieu et al.,
2016). This is consistent with prior reports of difficulties with intention understanding in ASD
(Koster-Hale et al., 2012; Margoni & Surian, 2016; Young et al., 2010).
Studies on empathy have shown that while cognitive empathy (understanding why
someone feels that emotion) may be affected in children with ASD, the capacity for affective
empathy (feeling and sharing the emotion of someone else) is relatively intact (Dziobek et al.,
2008; Rueda et al., 2015). Other studies suggest that the preservation of affective empathy is for
positive emotions alone (Mazza et al., 2014), and that both emotional and cognitive empathy are
impaired in ASD (Bos & Stokes, 2019) and that alexithymia, which is common in ASD, may
account for deficits in emotional empathy (Mul et al., 2018; Oakley et al., 2016). Emotional
differentiation, a trait affected in individuals with alexithymia, also has been found to prevent
individuals from making harsher moral judgments after being exposed to and primed with a
disgust-eliciting stimuli (Cameron et al., 2013). Thus, the relative prevalence of alexithymia
(difficulty describing one’s own and other people’s emotions; Sifneos, 1973) in the ASD
population could complicate empathic and emotional moral decision-making tasks (Dempsey et
al., 2020; Griffin et al., 2016). The trolley problem is a thought experiment that places one in the
role of a bystander that must choose between killing five people or one person from being run over
34
by an oncoming trolley (Thomson, 1985). One study found that autistic and alexithymic traits had
opposite effects on utilitarian decision-making during the trolley problem, with higher alexithymic
traits (rather than autistic traits) resulting in more utilitarianism (Patil et al., 2016). However, a
recent review of moral decision making (Dempsey et al., 2020) in ASD found that individuals on
the spectrum with and without alexithymic traits make the similar emotion-backed moral
judgments as neurotypical individuals, but may process emotional information differently than
neurotypicals (Schneider et al., 2013). In the study, during a moral decision-making task,
individuals with ASD showed relatively lower activations within the amygdala consistent with
potential impairments in empathic ability, but exhibited increased activity in the anterior and
posterior cortices of the cingulate gyrus associated with self-referential processing (Schneider et
al., 2013). This finding has led researchers to hypothesize that individuals with ASD, compared to
their neurotypical counterparts, may rely on an alternate neurocircuitry for emotional and moral
decision-making in ASD.
According to Haidt’s intuitionist approach (Haidt, 2001) and the notion of “autism as a
culture” (Davidson, 2008; Jaarsma & Welin, 2012), instead of perceiving children with ASD as
deficient in certain types of moral reasoning, we may instead conclude that within the culture of
autism, different foundational domains are prioritized that influence their moral development
(Dempsey et al., 2020). For example, from an intuitionist perspective, respecting authority and
following the rules could be the motivating factor for individuals with ASD to make more
outcome-based moral decisions and rule-bound rationales (Bellesi et al., 2018; Fadda et al., 2016;
Margoni & Surian, 2016). Indeed, there is evidence that hunter-gatherer societies also engage in
outcome-based (rather than intention-based) moral judgment (Barrett et al., 2016), consistent with
the notion that such processes may be cultural rather than related to a “deficiency”. This unique
35
neurodiverse perspective revamps the idea of an “impaired moral reasoning” faculty in ASD and
offers an alternate approach towards teaching moral values to children with ASD.
Given the tendency to make harsher judgments of unintentional actions, children with ASD
often face difficulties in forming social relationships (Bellesi et al., 2018). They may also have
difficulty modulating cooperative behavior based on their reading of moral transgressions of other
children (Li et al., 2014). Additionally, children with ASD have been shown to exhibit lower foodrelated disgust responses compared to typically developing children (Kalyva et al., 2010).
Acknowledging the association of disgust sensitivity with physical and moral disgust (Ottaviani et
al., 2013) and the link between morality and disgust (Haidt, 2001; Nichols, 2002), the
underrepresentation of morally disgusting triggers within the mind of a child with ASD may impair
learning about societal norms. Thus, it may be that differences in core disgust processing, along
with difficulties with intention understanding/ToM, make children with ASD prefer utilitarian
outcome-based judgments rather than emotionally driven and/or intention-based judgments.
1.7. Future Directions
In this review of literature on disgust processing, we have described a brief history of the
development of a definition for disgust, as well as highlighted the different domains under which
disgust-eliciting stimuli may fall. Following this, we elaborate on the nature of disgust proneness,
vicarious learning of disgust, disgust-related behaviors (including relationships with
psychopathology and morality). We further discuss underlying neural correlates of disgust
processing. Lastly, we reviewed the current disgust literature on the experiences and behaviors of
the ASD population.
36
Children with ASD are prone to infrequent contamination-based avoidant behaviors
towards potentially harmful stimuli (Kalyva et al., 2010), which can lead to further GI distress,
disease or toxicosis. Such conditions may lead to harmful downstream effects to personal health
or maintenance of social relationships. Additionally, children with ASD also have an early
attentional bias against attending to the disgusted facial expression (Xu et al., 2015; Zhao et al.,
2016), thus choosing to avoid the disgusted faces of other people. This bias away from attending
to disgusted faces, especially those of one’s caregivers, precludes habituation and the proper
processing of disgusted faces required for vicarious learning of disgust (Zhao et al., 2016) during
the development of children with ASD. Given this information, we gain an idea of how inefficient
disgust processing and disgust learning in children with ASD can affect their physical, mental and
social health in their early lives. Furthermore, the paucity of research into the nature of disgust
processing and the neurocognitive mechanisms underlying disgust regulation in children with ASD
hinders the development of evidence-based interventions. Thus, to adequately understand the
emotional experiences of children with ASD and its effect on their behaviors, it becomes important
to investigate the nature of disgust cognition and proneness in children with ASD to further
comprehend the underlying neurobiology and potential behavioral patterns associated with disgust
in ASD.
The use of the multidimensional nature of disgust proneness (propensity, sensitivity and
reactivity) in future research could help expand on the complexity of disgust processing in children
with ASD, as well as neurotypical children (Georgiadis et al., 2020; Olatunji, Ebesutani, et al.,
2016; Viar-Paxton & Olatunji, 2016). Additionally, identifying the neural circuitry underlying the
different dimensions of disgust through behavioral study or neuroimaging may help us better
understand the dimensional nuances on a psychobiological scale (Curtis, 2011; Davey, 2011;
37
Vicario, Rafal, Borgomaneri, et al., 2017). Such knowledge could then be used to investigate the
neural differences between children with ASD and neurotypical children along dimension-specific
neurocircuitry and its potential effect on food, social and moral behavior (Kalyva et al., 2010;
Olatunji, Puncochar, et al., 2016; Olatunji & Sawchuk, 2005; Vicario, Rafal, Martino, et al., 2017).
This also will help develop a body of information that could be used to identify alternative
evidence-based strategies to improve FER of disgust and reinforce disgust learning, eventually
leading to improvements in the quality of life of children and adolescents with ASD (Harms et al.,
2010; Kalyva et al., 2010).
Previous sections also discussed the differences in moral processing in individuals with
and without ASD. The repertoire of difficulties faced by individuals with ASD includes challenges
with mentalizing about others’ intentions and empathic processing (Dziobek et al., 2008; Mazza
et al., 2014; Senju et al., 2009). Difficulties with intention understanding in moral situations leads
to more outcome-based judgments, often resulting in harsher consequences for unintentional moral
violations rather than intentional moral violations that cause benign outcomes (Bellesi et al., 2018;
Dempsey et al., 2020; K. Gray et al., 2012). Additionally, children with ASD have trouble
engaging in cooperative behavior with other children due in part to their tendency for outcomebased judgments of moral transgressions (Li et al., 2014). Pragmatically speaking, in the real
world, such moral judgments can result in awkward social interactions, loss of friendships or
acquaintances, and social ostracism.
Considering these implications, further study into the influence of the disgust emotion on
moral decision-making, social behavior and behavioral modulation is required. Since moral disgust
is a fairly new concept (Olatunji et al., 2012; Scott, 2019), especially in research on children with
ASD, there is a dearth of literature on moral disgust behaviors in ASD which makes it difficult to
38
assess the influence of the disgust emotion on potential behavior. Studies on adults have found that
hyposensitive disgust responses have been related to individual violent and immoral acts, like
physical and verbal aggression, domestic violence tendencies and daily aggressive behavior (Pond
Jr. et al., 2012). Conversely, abnormally higher feelings of disgust could lead to higher incidences
of immoral acts such as lying or cheating, justified in the name of perceived self-preservation
(Winterich et al., 2014). While these studies were not conducted using an ASD cohort, the
incidence and unpredictability of aggressive behaviors is an established part of ASD
symptomatology (Farmer & Aman, 2011; Goodwin et al., 2019; Hill et al., 2014; Mayes et al.,
2012; Singh et al., 2011). Research on aggression in ASD is ongoing and recent studies have made
efforts to define predictors of these behaviors (Farmer & Aman, 2011; Goodwin et al., 2019; Hill
et al., 2014). Although more research is required in this area, especially given the relationship
between aggression and disgust (Pond Jr. et al., 2012), perhaps identifying how feelings of disgust
and emotion regulation during disgusting experiences relate to these behaviors in ASD may be an
interesting route.
Greater understanding of the nature of disgust processing in children and adolescents with
ASD could help predict and assess the risk of problematic behavior during their development.
Upon identifying moral disgust as a risk factor, intervention programs can be developed to target
individual-specific moral disgust triggers that could lead to harsh judgment, aggression, etc. Since
individuals with ASD are capable of intention-based judgments in thoroughly explained situations
(Margoni et al., 2019), one potential route for intervention strategies targeting moral understanding
in children with ASD may involve detailed explanations of the intentions and factors influencing
particular moral situations to help the children better understand these social nuances and possibly
influence better social outcomes (e.g. better relationships with classmates). Additional strategies
39
could include social tolerance-based behavioral interventions that would reduce the levels of
prejudice or felt disgust towards triggers (Mazzoni et al., 2020; Vasiljevic & Crisp, 2013). Given
the differences in moral understanding and decision-making in children with ASD, exploring the
neurobiological underpinnings of these behavioral differences could help discern the nature of
moral disgust in the ASD mind, including the possibility of a culturally-unique (Davidson, 2008;
Jaarsma & Welin, 2012) functional network. Additional research on the neural correlates of moral
disgust and how they related to aggressive behavior could also help to inform cognitive behavioral
interventions targeting the individual’s felt frustration and mood regulation (Scime & Norvilitis,
2006; Sukhodolsky et al., 2016) after adverse physical, social or moral disgust experiences.
This review of disgust processing in ASD is not exhaustive and additional dimensions to
disgust processing in neurotypical and ASD individuals may exist within the literature. However,
we have highlighted what in our opinion are the key areas regarding disgust processing, disgust
proneness and how these feelings influence behaviors in children with and without ASD. One of
the current challenges facing us is the lack of consistency in neurobiological findings associated
with disgust processing, especially the involvement of the insula in core and sociomoral disgust
processing (Cauda et al., 2012; Chapman et al., 2009; Menon & Uddin, 2010; Schaich Borg et al.,
2008). Further investigation is required to identify a unique general disgust processing network or
overlapping, yet domain-specific processing networks. Additionally, disgust proneness is highly
influenced by sociocultural factors (Haidt et al., 1997) that may bias findings depending on the
disgust-eliciting stimuli, either core or moral, and the population being studied. Another limitation
in moral decision-making studies with children with ASD is the focus on high-functioning and
verbal children which limits the scope of findings to a subset of the clinical population. Thus,
future studies investigating the experience of disgust towards specific stimuli should account for
40
the effects of individual difference factors, such as sociocultural norms surrounding the
perceptions of different physical (eg. smell of asafoetida) or moral (eg. mask mandate during a
pandemic) disgust stimuli.
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CHAPTER 2: NEURAL CORRELATES OF PHYSICAL AND SOCIAL DISGUST
PROCESSING IN YOUTH WITH AUTISM (ASD)
Abstract
Autism is characterized by difficulties in social communication and interaction, with
often differential responses to emotional stimuli. Disgust, a complex emotion associated with
aversion, plays a crucial role in social interactions and has been linked to altered eating and
social behavior in autistic youth. Understanding the neural mechanisms underlying disgust
processing in autistic youth is crucial for comprehending the divergent socio-emotional
processing. Using functional magnetic resonance imaging (fMRI), this study examined brain
activation patterns in response to physical and social disgust visual stimuli in a sample of autistic
youth (ASD; n = 25) and non-autistic counterparts (typically developing [TD]; n = 24).
Participants were presented with images depicting physical disgust (disgusting food) and social
vicarious disgust (disgusted facial expressions) while fMRI was acquired. Behaviorally, the ASD
group exhibited increased disgust sensitivity. Neuronally, significant differences in neural
activation between the ASD and TD groups included: decreased activity in emotion-related
regions (anterior [AI] and mid-insula [MI], ventromedial prefrontal cortex [vmPFC], medial
orbitofrontal cortex [mOFC], amygdala) and increases in posterior insula (PI) during physical
disgust; increased activity in AI, anterior cingulate, vmPFC/mOFC regions and decreases in face
processing areas, amygdala, PI and MI regions during social disgust. Further, in the ASD group,
activity in the right MI and bilateral mOFC during physical and social disgust was significantly
negatively correlated with disgust proneness. Taken together, the results indicate that reduced
disgust sensitivity in the autism group may result from activity differences in the MI and mOFC.
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By elucidating the specific neural mechanisms associated with disgust processing differences in
autism, this study contributes to the growing body of research aimed at unraveling the complex
neurobiology of autism and may inform future research and therapeutic interventions targeting
socio-emotional processing in this population.
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2.1. Introduction
Disgust is a negative basic emotion, mostly associated with “bad taste” (Ekman, 1992;
Ekman & Cordaro, 2011; Izard, 2011; Knowles et al., 2019; Levenson, 2011; Olatunji &
Sawchuk, 2005). Autistic children may show differences in disgust processing, potentially
leading to pica behaviors, gastrointestinal (GI) disturbances, and socio-emotional issues
(Dimopoulou et al., 2006; Madra et al., 2020). Young autistic individuals have potentially lower
disgust proneness (individual-specific disgust traits; Dimopoulou et al., 2006) and are more
likely to eat items that were not meant for consumption, with about 23% displaying pica
behaviors (Fields et al., 2021). Yet, the literature on disgust experiences in autism is considerably
limited (Vicario, Rafal, Martino, et al., 2017), due to researchers commonly studying disgust
alongside other basic emotions and general emotion and sensory processing differences (Harms
et al., 2010; Kalyva et al., 2010; Zhao et al., 2016). In this study, we investigated differences in
neural processing in autism as compared to a neurotypical group in two distinct contexts: foodrelated physical disgust, and face recognition of disgust (social disgust). We further measured
group differences in disgust proneness, and how disgust proneness impacts neural activity when
probed with disgust-related stimuli.
2.1.1. Food-related disgust in autism
One of the ways children learn about food and contamination-related behaviors during
development is social learning and communication (Rozin, 1990; Rozin & Millman, 1987;
Siegal, 2008), both of which may be considerably challenging for young autistic individuals
(Russo et al., 2009; Watson et al., 2003) and may underlie their diminished contamination
sensitivity (Kalyva et al., 2010; Siegal et al., 2011). Additionally, about 53-94% of autistic
44
individuals experience sensory sensitivities (Kirby et al., 2022), some of which may contribute to
the feeling of disgust. Olfactory and gustatory sensitivities in particular may play a critical role in
the development of food behaviors in autistic children (Chistol et al., 2018; Luisier et al., 2015).
Particularly, the appraisal of food odors as pleasurable or unpleasant, with the unpleasantness
leading to strong disgust feelings, and individual olfactory sensitivity was found to significantly
influence reluctance to eat new foods in autistic children, but not in the comparison group
(Luisier et al., 2015; Stafford et al., 2017). However, it has not been established if in autism
sensory sensitivities in olfaction or gustation are necessarily linked to higher disgust proneness
such as higher than usual disgust sensitivity and reactivity to smelly odors, and further research
is needed..
On the other hand, tactile sensitivity may influence feelings of disgust towards food
items, both normal and spoiled (Coulthard et al., 2022; Martins & Pliner, 2006). Individual traits
of textural sensitivity have been shown to predict eating patterns and pickiness in autistic
children (Chow et al., 2022; Mayes & Zickgraf, 2019; Schmitt et al., 2008). For example, in a
study on autistic males, there was a high degree of avoidance of eating foods with mushy, soft
textures and preference for crunchy, hard textures (Baraskewich et al., 2021; Schmitt et al.,
2008). Indeed, one study indicates that tactile sensitivity is linked to disgust processing in autistic
children (Martins & Pliner, 2006). Given that there are many food selectivity issues in autism
(Cermak et al., 2010; Kalyva, 2009), further studies are needed to better understand how disgust
processing may serve as a mediating factor or vice versa.
Neuroimaging evidence in neurotypical adults suggests that differences in functional
integrity and connectivity within the anterior insula (AI) can result in divergent disgust
experiences, for both core and vicarious disgust (Lamm & Singer, 2010; Sarinopoulos et al.,
45
2010; Shoemaker, 2012). Further, individual differences in disgust proneness, socio-cultural
factors, and educational environments may modulate neural activity associated with disgust (V.
Curtis, 2011; Davey, 2011; Vicario, Rafal, Borgomaneri, et al., 2017; Vicario, Rafal, Martino, et
al., 2017). In autistic individuals, differences in functional integrity of the AI have been
consistently found to be associated with difficulties with emotion, empathic processing, and
social processing, including physical disgust-related processing (J. S. Anderson, Druzgal, et al.,
2011; J. S. Anderson, Nielsen, et al., 2011; Bird et al., 2010; Caria & de Falco, 2015; Ebisch et
al., 2011; Lassalle et al., 2019; Molnar-Szakacs & Uddin, 2022; Nomi et al., 2019). Hence,
atypical functional activity and connectivity in the AI in autistic youth may underlie difficulties
with disgust emotion processing.
2.1.2. Recognition of disgusted facial expression in autism
Autistic individuals commonly show reduced sensitivity to processing others' negative
emotions, which may further cause difficulties with disgust learning (Losh & Capps, 2006;
Uljarevic & Hamilton, 2013). Many studies have found compelling evidence of differences in
processing disgusted facial emotion recognition (FER) in autism (Ashwin et al., 2007; Enticott et
al., 2014; Harms et al., 2010; Law Smith et al., 2010; White et al., 2015; Yeung et al., 2020;
Zhao et al., 2016). Autistic individuals exhibit increased attention bias and hypervigilance when
viewing disgusted facial expressions, which may lead to avoidant gaze behavior and disgustspecific FER difficulties above and beyond face processing differences also seen in autism
(Yeung et al., 2020; Zhao et al., 2016). Such differences in processing disgusted facial
expressions may impact other downstream socio-emotional processing.
46
Neuronally, many brain regions including the anterior cingulate cortex (ACC),
precuneus, fusiform gyrus, inferior frontal gyrus (IFG), and amygdala may be differentially
activated in FER in autism (Ashwin et al., 2007; Dapretto et al., 2006; Greimel et al., 2010; Hubl
et al., 2003; Kilroy et al., 2021; Monk et al., 2010; Nomi & Uddin, 2015; Patriquin et al., 2016;
Pelphrey et al., 2007; Pierce & Redcay, 2008; Piggot et al., 2004; Wang et al., 2004), and
differences may be impacted by development (Bastiaansen et al., 2011). However, most studies
have focused on numerous facial expressions, and have not specifically examined disgusted
facial expressions, making it difficult to infer disgust-specific neuronal FER differences in
autism.
2.1.3. Present study
Here, we examine potential disgust processing differences both behaviorally and
neuronally between typically-developing (TD) children and autistic children (ages 8-17 years).
First, we investigate behaviorally, between-groups differences in disgust proneness. Given that
autistic children have previously exhibited reduced contamination sensitivity (Kalyva et al., 2010),
we predict that the ASD group will show lower disgust proneness traits. Second, we investigate
between-group differences in brain regions underlying food-related or FER-related disgust
processing with an fMRI task, using four types of visual stimuli: neutral faces, disgusted faces,
neutral foods, and disgusting foods. We focused on detecting differences in neural activity within
the emotion-related brain regions (anterior insula, cingulate cortices, amygdala, prefrontal
cortices). Based on extant literature on reduced contamination sensitivity in autistic children
(Kalyva et al., 2010), we predict that as a group, autistic children would show reduced activity in
the anterior insula (AI) and other emotion processing regions (vmPFC, OFC, amygdala) when
47
looking at images of disgusting food. By contrast, while looking at images of others with disgusted
facial expressions, we predict increased activity in the AI, ACC, amygdala, and other emotion
processing regions and reduced activity in fusiform areas. This hypothesis is in line with higher
attentional biases and hypervigilance to facial expressions that result in disgust-specific FER
difficulties in autistic children (Yeung et al., 2020; Zhao et al., 2016). In addition, we predict that
individual differences of disgust proneness will be associated with activity patterns in the AI.
Specifically, when processing disgusting foods and disgusted facial expressions, we expect a
typical excitatory association where increased disgust proneness is correlated with increased
activity in the AI.
2.2. Materials and Methods
2.2.1. Participants
All participants were between ages 8-17 years, Our age range was broad enough for us to
meet recruitment goals. From here on, we use ‘youth’ to refer to our participant samples.
Autistic participants (ASD; n=25, 5 female)
Autistic youth were eligible if they had: (a) clinical diagnosis of ASD confirmed by the
Autism Diagnostic Observation Schedule-2 (Lord et al., 2012) and Autism Diagnostic Interview
- Revised (Lord et al., 1994); (b) IQ≥80 on the Wechsler Abbreviated Scale of Intelligence, 2nd
Edition (WASI-II, Wechsler, 2011); (c) no prior or concurrent diagnosis of other major
neurological, psychiatric, or developmental disorders (e.g., schizophrenia, brain tumor, and
epilepsy) mentioned during pre-screening of participants; (d) no known structural brain
48
abnormalities (e.g., aneurysm) after T2 scan review by in-house neuroradiologist; (e) righthanded as determined by a modified Oldfield questionnaire (Oldfield, 1971); and (f) Englishspeaking youth and parents, as our standardized measures are in English only. An IQ cut-off of
≥80 was used to ensure that participants could understand task directions, and to match IQ
between ASD and TD groups.
Age- and sex-matched controls (TD; n=24, 12 female)
The control group of non-autistic youth was matched in range and mean for age, sex, and
IQ with the ASD group. Other inclusion criteria included: (a) no ADHD as identified by the
parent through screening with the Conners 3rd edition (Conners et al., 2011); (b) no first-degree
relatives with ASD and no current or previous concerns about an ASD diagnosis; and the (c)-(f)
criteria pertaining to the ASD group.
2.2.2. Behavioral measures
Individual Differences in Disgust Processing. Individual traits of disgust perception were
assessed by the following measures: (1) Disgust Propensity and Sensitivity Scale – Revised
Child version (DPSS-R; Georgiadis et al., 2020; Olatunji et al., 2007), a self-report questionnaire
designed to measure disgust proneness traits - the frequency of disgust experiences (disgust
propensity, or DP) and the emotional impact of disgust stimuli (disgust sensitivity, or DS). It has
15 items scored on a five-point Likert scale (Cavanagh & Davey, 2000; Olatunji et al., 2007).
Both subscales maintained high internal consistencies and test-retest reliabilities (DP ICC = 0.69,
DS ICC = 0.77; van Overveld et al., 2006), and had moderate convergent validity with the
Disgust Scale (Haidt et al., 1994) and Disgust and Contamination Sensitivity Questionnaire
49
(Rozin et al., 1984).; and (2) Disgust Emotion Scale for Children (DES-C; Kleinknecht et al.,
1997; Muris et al., 2012), a self-report questionnaire that consists of 30 items in a five-factor
structure with the following subscales: blood, injury, and injections; odors; animals; mutilated
bodies; and rotting foods; each rated on a five-point Likert scale. The overall internal consistency
of the DES-C is high (⍺ = 0.93), with subscale internal consistencies ranging from 0.77 (animals)
to 0.91 (rotting foods). Assessment of the construct validity showed that over 90% of the items
loaded on to the desired factor. Only the odors and rotting foods sub-scales were included in
further analyses in order to match with the disgusting foods depicted in the fMRI task.
Sensory Sensitivities. Individual traits of sensory sensitivities were measured by the
Sensory Experiences Questionnaire Version 3.0 (SEQ; Ausderau et al., 2014; Baranek, 2009;
Baranek et al., 2006) which is a 105-item parent report, scored on a five-point Likert scale,
measuring four sensory response patterns (hypersensitivity; hyposensitivity; sensory interests,
repetitions and seeking; enhanced perception), five modality-specific sensory sensitivity to
quotidian, naturally-occurring sensory stimuli (gustatory, tactile, visual, auditory, vestibular),
and two sensory contexts (social & non-social). Factor analysis showed that the factor structure
of the SEQ had a reasonable fit, with strong (>0.2) and significant (p<0.001) factor loadings. We
used all aforementioned subscales in initial analysis to assess high multicollinearity, following
which only the gustatory and tactile subscales were used in partial correlations as proxies due to
their behavioral relevance to disgust.
Additional Measures: Interoception, Alexithymia, Theory of Mind (ToM). Individual
differences in interoceptive awareness, alexithymic traits, and ToM were measured using the
Body Perception Questionnaire Very Short Form (BPQ-VSF; Cabrera et al., 2018; Porges,
1993), the Alexithymia Questionnaire for Children (AQC; Rieffe et al., 2006), and the ToM total
50
score of the NEPSY-II (Korkman et al., 2007) respectively. The BPQ-VSF is a 12-item child
report with high test–retest reliability (ICC= 0.97) and with good internal consistency in an
American sample (ω = 0.91; Cabrera et al., 2018). The AQC, adapted from the Toronto
Alexithymia Scale, is a three-point Likert scale with three subscales: difficulty identifying
feelings (AQC ID), difficulty describing feelings (AQC Comm), and externally-oriented thinking
(AQC EOT) with internal consistencies (ɑ) of 0.73, 0.75 and 0.29 respectively. The AQC EOT
subscale was excluded due to its low consistency, and a sum total score of the two other
subscales (AQC 2-factor) was calculated (C. D. Butera et al., 2023; Loas et al., 2017). The ToM
behavioral assessment is part of the Social Perception domain of the NEPSY with test–retest
reliability ICC ≥ 0.5 and decent internal reliability (r ≥ 0.8) in ages 7–16 years.
2.2.3. MRI protocol
All data were acquired with a 3-Tesla Siemens MAGNETOM Prisma System (Siemens
Medical Solutions, Erlangen, Germany) using a 20-channel head coil. Functional volumes were
acquired continuously with the following parameters: TR=2s, TE=25ms, flip angle=90°, FOV
64x64 matrix, in-plane resolution 3x3mm, and 41 transverse slices, each 3mm thick, covering
the whole brain. We also acquired a structural T1-weighted MPRAGE in each subject
(TR=2.53s, TE=3.09ms, flip angle=10°, FOV 256x256 matrix, 208 coronal slices, 1mm isotropic
resolution).
2.2.4. fMRI task
Stimuli. Images were developed in four categories: neutral foods, disgusting foods (e.g.,
rotten meat), neutral expression faces, and disgusted expression faces (Figure 3). All stimuli
51
were overlaid on a white background, following methodology in previous studies (Vicario,
Rafal, Borgomaneri, et al., 2017; Vicario, Rafal, Martino, et al., 2017; Wicker et al., 2003). The
neutral and the disgusted facial expressions were chosen from an online repository (NimStim;
Tottenham et al., 2009) and from previous research (‘EmStim’; Kilroy et al., 2021), then edited
and counterbalanced so that each participant saw the same actor depicted displaying a neutral
and disgusted facial expression. For each participant, 18 images were used from each stimulus
category. Additionally, to ensure that the neutral food images were indeed items the participant
truly had no preferential or disgusting feelings for, all participants were administered a
questionnaire prior to participating in the study, asking them their preferences for each neutral
food pictured in the stimuli. Only items neutral to the participant were included in the “neutral
food” category.
fMRI Task. One fMRI run was presented to all participants, six blocks per stimulus
category (disgusting foods, neutral foods, disgusting facial expressions, neutral facial
expressions). Within each 15-second block, three different images from the same category were
presented with a 250-millisecond fixation crosshair between each stimulus (e.g., three different
disgusting food images). Thus, the fMRI task consisted of 24 blocks (5 per stimulus category),
lasting for a single 10-minute run. Prior to scanning, all participants completed a mock scanning
session to familiarize them with the scanning environment and to help reduce head motion
artifacts.
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Figure 3. Examples of fMRI stimuli divided into the four categories - disgusting foods and faces,
and neutral foods and faces
2.2.5. fMRI data analysis
We expected ~10% of ASD participants to exhibit high head motion (Kilroy et al.,
2021). Participants who exhibited extreme in-scanner head motion (absolute head motion > 1.5
mm and relative head motion > 0.3 mm) were excluded from data analysis. No significant
differences in absolute (t = -0.548, p = 0.293) and relative (t = -0.952, p = 0.173) head motion
were found between the two groups after exclusion of high head motion subjects (excluded n =
3; not included in participant counts). All analyses followed best practices in fMRI analysis, as
53
detailed in our prior studies (Kilroy et al., 2021). The data analytic approach used to address each
of our research questions utilized FMRIB’s Software Library 6.0 (FSL; Jenkinson et al., 2002,
2012; Jenkinson & Smith, 2001; Smith et al., 2004; Woolrich et al., 2004) and entailed: (i)
Within-Subject Analyses/Preprocessing; (ii) Within-Group/First-level Analyses; and (iii)
Between-Group Analyses. Standard preprocessing pipeline was performed involving: (a)
structural T1 brain extraction and non-brain tissue removal; (b) smoothing with 5 mm FWHM
Gaussian kernel; (c) B0 unwarping along y-axis; (d) high pass filter with 100 sec cutoff; (e)
realignment using MCFLIRT to obtain motion estimates; (f) Independent component analysis
(ICA). Preprocessed data was fed into the ICA AROMA algorithm (Pruim et al., 2015), which
filtered out noise and motion components from the whole brain signal. Registration to the MNI152 standard atlas using 12 degrees-of-freedom affine transformation and FNIRT nonlinear
registration (Jenkinson et al., 2002; Jenkinson & Smith, 2001) were performed. First-level
analysis included modeling the stimulus conditions for each participant as separate regressors
(Disgust Food, Neutral Food, Disgust Faces, Neutral Faces, Disgust, Neutral), derived by
convolving a double gamma function with the block task design to render the hemodynamic
responses, along with their respective temporal derivatives. Subject-specific head motion
parameters were used as nuisance regressors.
Between-group comparisons between the TD and ASD groups were performed using
higher level mixed-effects analyses with FSL's FLAME 1 algorithm. These results were assessed
for the main effects of the disgust conditions (disgusting foods>rest and disgusted faces>rest)
and the contrasts against neutral stimuli (disgusting foods>neutral foods, disgusted faces>neutral
faces). Comparison against neutral stimuli were made to help determine brain activations
associated with the disgust stimuli while accounting for activations due to general processing of
54
that particular type of stimuli (e.g., disgusting foods rather than just foods). Significance of
whole-brain voxel-wise activity differences was tested at threshold of Z>3.1 (equivalent to pvalue less than 0.001) with a corrected cluster size probability threshold of p<0.05. We used age
as a covariate in the analysis, along with sex and IQ.
For regions of interest (ROI), an additional small volume correction (SVC) analysis was
performed with a significance threshold of p<0.05 using a predefined mask. The small volume
mask was defined utilizing the Neurosynth database (which performs automated large-scale
meta-analyses of fMRI data) results for search terms “disgust”, “emotional faces”, and “food”, as
well as insula parcellations from previous research (Deen et al., 2011). Final mask included the
insular divisions (dAI, vAI, PI), dlPFC, vmPFC, mOFC, ACC, fusiform areas, amygdala,
hippocampus, putamen, nucleus accumbens, lingual gyrus, middle temporal gyrus, and superior
frontal gyrus. Activity within the MI was determined by manually viewing the neuroanatomical
extent of the activations in the insula (Uddin et al., 2017). We primarily focused on the insula
ROIs because of their role in processing disgust, interoceptive and exteroceptive sensory stimuli,
and emotions. Median activity within each ROI was extracted, evaluated across subjects for null
activity values, and selectively exported for statistical analysis. The median activity was chosen
over mean activity to mitigate bias caused by small cluster sizes. After extraction of median
activity from all selected regions, data were visually inspected to identify the proportion of nonzero points for each ROI. As only the ROIs with ≥90% of non-zero data were used in correlation
and regression analyses, three ROIs (bilateral amygdala and right dAI) were excluded for
disgusting food brain-behavior correlations and three ROIs (bilateral ACC, right PI) were
excluded for disgusted faces brain-behavior correlations due to a large proportion of zero data
points, resulting in strong floor effects.
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2.2.6. Statistical analysis
All statistical analyses and visualizations were performed using SPSS (Version 28) and R
(R Core Team, 2013). Behavioral and brain activity measures were initially assessed for
normality and linearity. For all behavioral measures, we used the raw total scores, except for IQ
for which age-normed scores were used. Missing data points were corrected using rough
imputation (roughfix in the randomForest package in R), as long as no more than 15% of the
data points were missing (no measures had missing data exceeding missing threshold).
Following satisfaction of linearity assumptions, Student’s t-tests were used to assess betweengroup differences in all behavioral measures. Pearson product-moment correlations were
performed to first assess associations between behavioral variables, then associations of behavior
with brain activity in the different ROIs selectively exported after fMRI data analysis. To adjust
for the issue of multicollinearity, only one of the collinear variables measuring the same behavior
was chosen as a proxy (for instance, gustatory or tactile hypersensitivity as a proxy for sensory
sensitivity measured by SEQ). For instance, DS and DP were chosen as the main disgust
behavioral variables in further analyses as they capture trait-level behavioral characteristics over
stimuli-specific sensitivities and were highly correlated with DES. Pearson partial correlations
were used to assess associations between brain activity and disgust processing, after adjusting for
potential behavioral (sensory sensitivity, alexithymia, interoception) and demographic (age, sex,
IQ) covariates. Linear regression was used to model brain activity associated with DS and DP for
each of the ROIs.
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2.3. Results
2.3.1. Behavioral & demographic variables
All behavioral and demographic variables satisfied linearity assumptions after z-score
normalization. Significant differences were observed between the TD and ASD groups for the
following variables: IQ (TD>ASD), sex (TD>ASD), DS (ASD>TD), NEPSY ToM (TD>ASD),
all SEQ scores (ASD>TD), and all AQC scores (ASD>TD). A full descriptive summary of the
behavioral results can be found in Table 2. DS was significantly higher in the ASD group as
compared to the TD group, while DP did not significantly differ between groups. Other than with
the DES subscores, DS was significantly correlated with all AQC scores (AQC 2-factor:
R=0.511, p<0.001; AQC ID: R=0.553, p<0.001; AQC Comm: R=0.35, p=0.014) and tactile
sensitivity (R=0.302, p=0.035), while DP was significantly correlated only with AQC scores
(AQC 2-factor: R=0.349, p=0.014; AQC ID: R=0.34, p=0.017; AQC Comm: R=0.288, p=0.045).
Correlations of behavioral variables across groups can be found in Table 3 and Figure 4A.
In the TD group (Figure 4B), DS significantly correlated with tactile sensitivity
(R=0.419, p=0.041) and alexithymic traits (AQC 2-factor: R=0.497 , p=0.014 ; AQC ID:
R=0.508 , p=0.011). By contrast, in the ASD group (Figure 4C), DS only significantly correlated
with alexithymic traits (AQC 2-factor: R=0.439 , p=0.028 ; AQC ID: R=0.514 , p= 0.009) and
had a positive trend with BPQ (R = 0.388, p=0.055). DP did not significantly correlate with the
behavioral variables when each group was observed separately.
57
Table 2. Descriptive summary of demographic and behavioral variables and group comparisons
Group Sig.
TD
(n = 24)
ASD
(n = 25) t-statistic p-value
Age 12.0 (2.1) 12.4 (2.5) -0.673 0.504
IQ 123.4 (15.5) 105.5 (13.7) 4.280 <0.001*
Sex 4.864† 0.027†
*
Male 12 20
Female 12 5
ADOS Social affect - 10.3 (3.1) - -
ADOS RRB - 1.9 (1.4) - -
ADOS Total - 12.2 (3.2) - -
Disgust sensitivity 10.5 (3.6) 14.1 (6.5) -2.391 0.022*
Disgust propensity 21.3 (4.3) 24.5 (7.8) -1.788 0.08
DES Rotting Foods 14.7 (4.9) 16.4 (5.6) -1.116 0.27
DES Odors 13.5 (5.3) 15.5 (5.2) -1.384 0.173
SEQ Hyper 1.4 (0.3) 2.5 (0.7) -7.330 <0.001*
SEQ Hypo 1.2 (0.1) 1.8 (0.5) -6.067 <0.001*
SEQ SIRS 1.3 (0.3) 2.2 (0.8) -4.930 <0.001*
SEQ EP 1.6 (0.4) 2.4 (0.8) -4.331 <0.001*
SEQ Social 1.4 (0.2) 2.3 (0.6) -6.855 <0.001*
SEQ Non-social 1.3 (0.2) 2.2 (0.6) -6.261 <0.001*
SEQ Sensory modalities
Gustatory 1.4 (0.3) 2.4 (0.8) -5.709 <0.001*
Tactile 1.4 (0.3) 2.3 (0.6) -7.409 <0.001*
Visual 1.2 (0.3) 2.1 (0.7) -5.455 <0.001*
Auditory 1.5 (0.4) 2.3 (0.7) -5.209 <0.001*
Vestibular 1.4 (0.2) 1.9 (0.6) -4.333 <0.001*
BPQ-VSF 28.1 (15.2) 27.2 (10.8) 0.223 0.823
AQC 2-factor 6.6 (5.0) 11.1 (4.6) -3.296 0.002*
AQC Identification 0.5 (0.4) 0.8 (0.5) -2.494 0.016*
AQC Communication 0.7 (0.5) 1.1 (0.4) -3.575 0.001*
NEPSY ToM Total 26.0 (1.9) 22.0 (3.6) 4.912 <0.001*
† Pearson �2 test statistic and p-value.
Note: t-tests comparisons between TD and ASD groups. ADOS = Autism Diagnostic Observation Schedule-2; RRB =
Restrictive and repetitive behavior; DES = Disgust Emotion Scale; SEQ = Sensory Experiences Questionnaire version 3.0;
Hyper = hypersensitivity; Hypo = hyposensitivity; SIRS = sensory interests, repetitions and seeking; EP = enhanced perception;
BPQ-VSF = Body Perception Questionnaire - Very Short Form; AQC = Alexithymia Questionnaire for Children; NEPSY =
NEuroPSYchological behavioral assessment; ToM = Theory of mind.
58
Figure 4. A. Correlation plot (lower triangle) of main behavioral variables across groups. B.
Correlations in TD group. C. Correlations in ASD group. DS = Disgust sensitivity; DP =
Disgust propensity; DES = Disgust Emotion Scale; SEQ = Sensory Experiences Questionnaire
version 3.0; Hyper = hypersensitivity; Hypo = hyposensitivity; SIRS = sensory interests,
repetitions and seeking; EP = enhanced perception; BPQ = Body Perception Questionnaire -
Very Short Form; AQC = Alexithymia Questionnaire for Children; ID = difficulty identifying
feelings; Comm = difficulty describing feelings; NEPSY = NEuroPSYchological behavioral
assessment; ToM = Theory of mind.
59
Table 3. Correlation matrix (lower triangle) for Pearson’s product-moment correlation of main
behavioral variables across groups
1.
Age 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22.
2. IQ -
0.026 - - - - - - - - - - - - - - - - - - - - -
3. Sex 0.124 0.024 - - - - - - - - - - - - - - - - - - - -
4. DS 0.123
-
0.282
*
-
0.0
47
- - - - - - - - - - - - - - - - - - -
5. DP 0.136
-
0.318
*
0.0
94
0.593*
** - - - - - - - - - - - - - - - - - -
6. DES
Rotting
Foods
-
0.035
-
0.326
*
0.0
34 0.335* 0.537*
** - - - - - - - - - - - - - - - - -
7. DES
Smells
-
0.129
-
0.426
**
0.0
77 0.339* 0.531*
**
0.742*
** - - - - - - - - - - - - - - - -
8. SEQ
Hyper 0.028
-
0.406
**
-
0.2
83*
0.198 0.252 0.055 0.17
3 - - - - - - - - - - - - - - -
9. SEQ
Hypo 0.167
-
0.398
**
-
0.2
65
0.056 0.076 0.055 0.07
7
0.786*
** - - - - - - - - - - - - - -
10. SEQ
SIRS 0.042
-
0.421
**
-
0.3
46*
0.263 0.214 0.039 0.07
7
0.815*
**
0.692*
** - - - - - - - - - - - - -
11. SEQ
EP 0.12 -
0.35*
-
0.3
48*
0.119 0.201 0.045 0.04
6
0.753*
**
0.621*
**
0.832*
** - - - - - - - - - - - -
12. SEQ
Social 0.052
-
0.381
**
-
0.3
32*
0.179 0.133 0.045 0.08
2
0.936*
**
0.848*
**
0.858*
**
0.786*
** - - - - - - - - - - -
13. SEQ
Non-social 0.071
-
0.442
**
-
0.3
4*
0.186 0.23 0.051 0.12 0.926*
**
0.803*
**
0.952*
**
0.885*
**
0.917*
** - - - - - - - - - -
14.
Gustatory 0.037
-
0.409
**
-
0.3
72*
*
0.085 0.236 0.072 0.16
4
0.886*
**
0.679*
**
0.83**
*
0.853*
**
0.81**
*
0.92**
* - - - - - - - - -
15. Tactile -0.01
-
0.476
**
-
0.3
25*
0.302* 0.254 0.111 0.21 0.929*
**
0.81**
*
0.874*
**
0.748*
**
0.934*
**
0.926*
**
0.825*
** - - - - - - - -
16. Visual 0.082
-
0.398
**
-
0.3
81*
*
0.157 0.182 0.07 0.09
4
0.846*
**
0.777*
**
0.927*
**
0.864*
**
0.906*
**
0.943*
** 0.8*** 0.846*
** - - - - - - -
17.
Auditory 0.229
-
0.346
*
-
0.2
31
0.162 0.134 0.038
-
0.00
6
0.798*
**
0.754*
**
0.865*
**
0.861*
**
0.855*
**
0.897*
**
0.78**
*
0.798*
**
0.868*
** - - - - - -
18.
Vestibular 0.043
-
0.353
*
-
0.2
01
0.213 0.146 -0.047 0.00
9
0.86**
*
0.771*
**
0.877*
**
0.789*
**
0.866*
**
0.914*
**
0.792*
**
0.835*
**
0.868*
**
0.823*
** - - - - -
19. BPQ 0.042 -
0.066
0.1
46 0.186 -0.046 0.134 0.03
9 -0.151 -0.092 -0.126 0.004 -0.134 -0.114 -0.143 -0.096 -0.075 -0.12 -0.056 - - - -
20. AQC
2-factor 0.05 -
0.134
-
0.0
68
0.511*
** 0.349* 0.076 0.26
5
0.464*
* 0.269 0.38** 0.206 0.314* 0.399*
*
0.387*
*
0.421*
* 0.328* 0.253 0.378
**
0.03
1 - - -
60
21. AQC
ID 0.073 -
0.121
-
0.0
03
0.553*
** 0.34* 0.146 0.30
8* 0.336* 0.141 0.344* 0.162 0.215 0.317* 0.295* 0.321* 0.277 0.189 0.319
*
0.15
4
0.932*
** - -
22. AQC
Comm 0.01 -
0.123
-
0.1
39
0.35* 0.288* -0.031 0.15
3
0.535*
**
0.379*
* 0.348* 0.222 0.378*
*
0.423*
*
0.428*
*
0.464*
* 0.327* 0.284* 0.375
**
-
0.13
7
0.88**
*
0.648*
** -
23.
NEPSY
ToM
0.148 0.515
***
0.3
32*
-
0.358* -0.158 -0.211
-
0.27
1
-
0.332*
-
0.305*
-
0.369*
*
-
0.322* -0.4** -
0.352*
-
0.319*
-
0.415*
*
-
0.377*
*
-
0.398*
*
-0.244 0.01
2 -0.202 -0.227
-
0.12
7
Note: * p<0.05; ** p<0.01; *** p<0.001
DS = Disgust sensitivity; DP = Disgust propensity; DES = Disgust Emotion Scale; SEQ = Sensory Experiences Questionnaire version 3.0; Hyper
= hypersensitivity; Hypo = hyposensitivity; SIRS = sensory interests, repetitions and seeking; EP = enhanced perception; BPQ = Body
Perception Questionnaire - Very Short Form; AQC = Alexithymia Questionnaire for Children; ID = difficulty identifying feelings; Comm =
difficulty describing feelings; NEPSY = NEuroPSYchological behavioral assessment; ToM = Theory of mind.
2.3.2. Between-group differences in fMRI
1. Whole-brain voxel-wise activation differences
At a threshold of Z>3.1, no activation differences were found between groups for the
disgusting foods condition and only increased activation in the ACC in the ASD group as
compared to the TD group was found in the disgusted facial expressions condition (Table 4 &
Figure 5).
Table 4. Whole-brain voxel-wise activation results for the main effect of Disgust Faces
Condition Contrast
Max Z
score
Cluster
size
(p<0.05) X Y Z Laterality Region
Disgust Faces>Rest ASD>TD 4.19 134 0 38 20 R Anterior cingulate cortex
61
Figure 5. Cross-sectional view along the sagittal axis (right-to-left) of whole-brain activation
results for the contrast of ASD>TD for the main effect of Disgust Faces, thresholded at Z>3.1,
cluster p<0.05
2. Small volume correction
a. Disgusting foods
When viewing disgusting food (as compared to rest or neutral foods) the following
regions had activations that were significantly greater in the TD group than in the ASD group:
right ventromedial prefrontal cortex (vmPFC), bilateral ventral anterior insula (vAI), right midinsula (MI), bilateral amygdala, left posterior temporal fusiform gyrus, and right medial
orbitofrontal cortex (mOFC). Greater activations were seen in the ASD group over the TD group
62
only in the bilateral posterior insula (PI); and bilateral dorsal anterior insula (dAI) when
compared to neutral foods.
As previously reported in extant literature, activation in the right ventromedial prefrontal
cortex (vmPFC) was reduced in the ASD group relative to the TD group, and activation in the
bilateral dorsal anterior insula (dAI) was increased in the ASD group relative to the TD group.
Figure 6B and Figure 6C depict the location and the cluster extent of the selected regions of
interest respectively. A full list of all small-volume correction results can be found in Table 5.
Table 5. Small-volume correction voxel-wise activations for the Disgust Food main effect and the
Disgust Food>Neutral Food contrast
Condition Contrast
Max
Z
Cluster size
(Z>1.96) X Y Z Laterality Region
Disgust Food > Rest TD>ASD 3.00 88 -33 13 -12 L Ventral anterior insula (AI)
2.42 7 -22 -8 -12 L Amygdala
3.04 12 -38 -22 -22 L Posterior temporal fusiform
cortex
2.91 17 -20 26 -18 L Medial orbitofrontal cortex
(mOFC)
2.78 6 -12 -96 -12 L Occipital pole
2.21 5 34 22 -2 R Dorsal AI
2.32 9 32 14 -10 R Ventral AI
3.42 44 42 -2 -8 R Mid-insula (MI)
2.24 8 20 6 -24 R Amygdala
2.95 49 20 28 -14 R mOFC
2.78 8 30 36 -20 R mOFC
2.67 13 4 14 68 R Superior frontal gyrus
2.96 10 18 -86 -16 R Occipital fusiform gyrus
3.02 5 26 62 -14 R Dorsolateral prefrontal cortex
(dlPFC)
ASD>TD 2.49 3 -36 -14 16 L Posterior insula (PI)
2.29 2 -52 -10 20 L Postcentral gyrus
2.58 2 -4 -78 -2 L Lingual gyrus
63
2.75 6 -4 62 -24 L Frontal pole
2.22 3 -20 48 -20 L Frontal pole
2.13 2 42 -14 2 R PI
Disgust Food >
Neutral Food TD>ASD 2.16 6 -40 12 -10 L Ventral AI
2.02 3 -26 -4 -20 L Amygdala
3.37 17 -38 -56 -8 L Posterior temporal fusiform
cortex
2.61 8 -22 40 -20 L Orbitofrontal cortex
2.60 7 -20 30 -12 L Orbitofrontal cortex
2.50 4 -6 4 -8 L Nucleus accumbens
2.15 4 -40 14 -40 L Temporal pole
2.80 7 -36 -84 -18 L Lateral occipital cortex
2.48 3 -40 -46 -12 L Temporal occipital fusiform
cortex
2.75 10 31 14 -16 R Ventral AI
3.17 25 42 -2 -2 R MI
2.35 16 6 60 -2 R Ventromedial prefrontal cortex
(vmPFC)
2.38 14 32 -2 -22 R Amygdala
2.56 8 28 38 -18 R mOFC
2.60 10 38 20 -44 R Temporal pole
ASD>TD 2.95 19 -40 2 -2 L Dorsal AI
2.57 12 -33 -2 8 L Dorsal AI
2.96 7 -37 -13 16 L PI
3.16 12 -18 -4 -32 L Anterior parahippocampal gyrus
2.76 2 -28 60 -10 L dlPFC
2.61 7 32 12 2 R Dorsal AI
2.39 13 36 -5 6 R PI
2.05 3 44 -12 8 R PI
64
Figure 6. A. Cross-sectional view along the sagittal axis (left-to-right) of small-volume
correction (SVC) activation results for both contrast: TD>ASD (red-yellow) and ASD>TD (blue-
65
light blue) during the Disgust Food>Rest condition at Z>1.96, cluster p uncorrected. B. Nodes
representing the ROIs chosen from SVC cluster activations for contrasts TD>ASD (blue) and
ASD>TD (red) during Disgust Food. dAI = dorsal anterior insula; vAI = ventral anterior
insula; MI = mid-insula; PI = posterior insula; Amyg = amygdala; mOFC = medial
orbitofrontal cortex; vmPFC = ventromedial prefrontal cortex. C. Glass brain representation of
the left Disgust Food ROIs, corresponding to the nodes in B., with cut-out for insular regions. D.
Glass brain representation of the right Disgust Food ROIs, corresponding to the nodes in B.,
with cut-out for insular regions.
b. Disgusted facial expressions
When viewing disgusted facial expressions (compared to neutral or to rest) , the
following regions were common and had activations that were significantly greater in the ASD
group than in the TD group: bilateral vAI, right dAI, left mOFC and right anterior cingulate
cortex (ACC). Greater activations were seen only in the right PI and right amygdala in the TD
group relative to the ASD group. We also found reduced activations in the right MI in the
disgusted faces main effect in the ASD group relative to the TD group; greater activations in the
left ACC in the disgusted faces main effect in the ASD group relative to the TD group; and
greater activations in the right vmPFC in the Disgust Faces>Neutral Faces contrast in the ASD
group relative to the TD group. Figure 7B and Figure 7C depict the location and the cluster
extent of the selected regions of interest respectively. A full list of all small-volume correction
results can be found in Table 6.
Table 6. Small-volume correction voxel-wise activations for the Disgust Faces main effect and
the Disgust Faces>Neutral Faces contrast
Condition Contrast
Max
Z
Cluster size
(Z>1.96) X Y Z Laterality Region
Disgust Faces > Rest TD>ASD 2.17 2 -12 -96 -12 L Occipital pole
2.14 4 35 -4 1 R PI
3.07 12 42 10 -14 R MI
2.51 2 44 -2 -6 R MI
66
2.50 15 22 -8 -12 R Amygdala
2.86 6 18 -86 -16 R Occipital fusiform gyrus
2.36 9 38 -58 -20 R Temporal occipital fusiform
gyrus
2.20 5 30 6 -20 R Temporal pole
2.29 4 14 26 -16 R mOFC
ASD>TD 3.15 116 -30 16 6 L Dorsal AI
2.84 6 -40 -14 0 L Dorsal AI
2.41 5 -36 2 -10 L Ventral AI
2.30 5 -10 42 0 L Anterior cingulate cortex
(ACC)
2.50 12 -26 40 -12 L mOFC
2.58 14 42 10 2 R Dorsal AI
4.00 49 42 18 -10 R Ventral AI
2.47 5 10 38 4 R ACC
3.12 10 56 -44 8 R Middle temporal gyrus
2.55 3 66 -54 -6 R Middle temporal gyrus
2.38 7 36 18 -42 R Temporal pole
Disgust Faces > Neutral
Faces TD>ASD 2.34 6 36 -4 4 R PI
2.23 4 21 -6 -14 R Amygdala
ASD>TD 2.09 5 -38 12 -12 L Ventral AI
2.25 2 -26 14 -14 L Ventral AI
2.30 4 -20 4 -8 L Putamen
2.17 6 -2 34 -20 L mOFC
2.04 3 -4 -80 -2 L Lingual gyrus
2.67 3 -12 -88 -12 L Lingual gyrus
2.24 6 42 18 2 R Dorsal AI
3.30 29 44 14 -10 R Ventral AI
2.50 5 26 18 -14 R Ventral AI
2.45 16 10 56 -8 R vmPFC
2.64 4 10 38 4 R ACC
2.54 6 30 6 -2 R Putamen
2.40 8 38 -62 -12 R Occipital fusiform gyrus
67
Figure 7. A. Cross-sectional view along the sagittal axis (left-to-right) of small-volume
correction (SVC) activation results for both contrast: TD>ASD (red-yellow) and ASD>TD (blue-
68
light blue) during the Disgust Faces>Rest condition at Z>1.96, cluster p uncorrected. B. Nodes
representing the ROIs chosen from SVC cluster activations for contrasts TD>ASD (blue) and
ASD>TD (red) during Disgust Faces. dAI = dorsal anterior insula; vAI = ventral anterior
insula; MI = mid-insula; PI = posterior insula; Amyg = amygdala; mOFC = medial
orbitofrontal cortex; vmPFC = ventromedial prefrontal cortex; ACC = anterior cingulate
cortex. C. Glass brain representation of the left Disgust Faces ROIs, corresponding to the nodes
in B., with cut-out for insular regions. D. Glass brain representation of the right Disgust Faces
ROIs, corresponding to the nodes in B., with cut-out for insular regions.
2.3.3. Brain-behavior correlations across groups
Brain-behavior correlations for selected ROIs can be found in Table 7 and Figure 8
respectively. When looking at disgusting foods, only two ROIs were found to be significantly
correlated with either DS or DP: the right MI (with DS: R = -0.33, p = 0.021) and the right
mOFC (with DS: R = -0.453, p = 0.001; with DP: R = -0.329, p = 0.021).
When looking at disgusted facial expressions, two ROIs were found to be significantly
correlated with disgust proneness for observation of disgusted faces: the left mOFC (with DS: R
= -0.355, p = 0.012) and the right MI (with DP: R = -0.309, p = 0.031).
69
Figure 8. A. Correlation plot of Pearson’s correlations between selected Disgust Food ROIs and
main behavioral and demographic variables. B. Correlation plot of Pearson’s correlations
between selected Disgust Faces ROIs and main behavioral and demographic variables.
70
Table 7. Correlation table of Pearson’s correlations of selected Disgust Food and Disgust Faces
ROIs and main behavioral and demographic variables
Disgust Foods Disgust Faces
Left
vAI
Left
dAI
Left
PI
Left
mOFC
Right
MI
Right
mOFC
Right
vmPFC
Right
PI
Left
dAI
Left
mOFC
Right
MI
Right
Amygdala
Right
dAI
Right
vAI
Right
vmPFC
1. DS -
0.196
0.08
2
0.23
2 -0.142 -
0.33*
-
0.453*
*
-0.04 -
0.132
-
0.00
2
-0.355* -
0.074 -0.022 -
0.027 0.021 0.119
2. DP -
0.025
0.04
1
0.16
4 -0.03 -
0.252 -0.329* 0.049 -
0.118
-
0.06
1
-0.241
-
0.309
*
-0.089 -
0.015 0.102 0.155
3. SEQ
Hyper
-
0.314
*
0.16
7
0.09
2 -0.356*
-
0.464
**
-0.351* -0.018 -0.04 0.35
1* 0.121 -
0.145 -0.345* 0.409
**
0.452
** 0.142
4. SEQ
Hypo
-
0.314
*
0.33
6*
0.14
4 -0.275 -0.23 -0.29* 0.018 0.037 0.17
6 -0.0002 0.019 -0.237 0.203 0.342
* 0.211
5. SEQ
SIRS
-
0.334
*
-
0.00
2
-
0.15
7
-0.43**
-
0.356
*
-
0.391*
*
-0.06 -
0.235 0.07 -0.072 -
0.189 -0.321* 0.196 0.263 0.227
6. SEQ
EP
-
0.32*
-
0.00
4
-
0.09
1
-
0.383*
*
-
0.321
*
-
0.393*
*
-0.101 -
0.125
0.05
8 -0.002 -
0.278 -0.32* 0.301
* 0.277 0.084
7. SEQ
Social
-
0.293
*
0.22
8
0.01
9 -0.363*
-
0.355
*
-0.313* -0.063 0.044 0.27
8 0.095 -
0.114 -0.311* 0.3* 0.42*
* 0.179
8. SEQ
Nonsocial
-
0.365
*
0.07
2
-
0.03
8
-
0.407*
*
-
0.4** -0.4** -0.039 -
0.163
0.17
5 0.006 -
0.195 -0.349* 0.309
*
0.351
* 0.187
9.
Gustat
ory
-
0.295
*
0.01
1
0.00
8 -0.292*
-
0.408
**
-0.344* 0.051 -
0.094
0.20
6 0.093 -
0.206 -0.414** 0.362
*
0.376
** 0.127
10.
Tactile
-
0.368
**
0.16
6
0.02
8 -0.349*
-
0.38*
*
-0.365* 0.004 -
0.056 0.27 0.036 -
0.136 -0.324* 0.282
*
0.343
* 0.138
11.
Visual
-
0.322
*
0.11
-
0.08
6
-
0.447*
*
-
0.382
**
-
0.387*
*
-0.096 -
0.117
0.15
6 0.022 -
0.165 -0.298* 0.248 0.356
* 0.226
12.
Audito
ry
-
0.314
*
0.04
7
-
0.07
9
-
0.447*
*
-
0.35*
-
0.421*
*
-0.119 -
0.109
0.07
3 -0.021 -
0.235 -0.318* 0.258 0.304
* 0.209
13.
Vestib
ular
-
0.286
*
0.11
-
0.07
3
-
0.372*
*
-
0.349
*
-0.356* -0.079 -
0.222
0.19
9 -0.062 -
0.137 -0.308* 0.369
** 0.33* 0.204
14.
BPQ 0.014 0.08 0.17
4 0.016 0.153 -0.109 -0.029 0.09
-
0.12
9
-0.107 -
0.002 -0.181 -
0.072 0.044 -0.144
15.
AQC
2-
factor
-
0.338
*
0.18
4
0.19
4 -0.187
-
0.47*
*
-0.313* -0.031 -
0.202
0.23
6 0.046 -
0.119 -0.19 0.242 0.191 0.273
16.
AQC
ID
-
0.244
0.16
3
0.18
7 -0.095
-
0.366
*
-0.322* -0.042 -
0.162
0.17
1 -0.006 -
0.125 -0.157 0.189 0.175 0.308*
17.
AQC
Comm
-
0.391
**
0.17
4
0.16
2 -0.268
-
0.508
***
-0.236 -0.01 -
0.211
0.27
1 0.104 -
0.085 -0.193 0.261 0.173 0.17
18.
NEPS
Y ToM
0.335
*
-
0.02
2
-
0.03
8
0.22 0.277 0.439*
* 0.023 -
0.078
-
0.11
2
0.01 0.033 0.284* 0.001 -
0.227 -0.097
19. Age -
0.009
0.09
4
0.39
1** -0.015 -
0.068 -0.091 -0.144 0.118 -
0.13 -0.225 -
0.004 0.17 -
0.086
-
0.042 0.081
20. IQ 0.177 0.00
2
-
0.15
7
0.084 0.067 0.342* -0.186 0.19
-
0.07
4
0.161 -
0.019 0.365* -
0.063
-
0.259 0.115
21. Sex 0.092 0.04
2
0.01
5 0.121 0.407
** 0.141 -0.276 -
0.172
0.00
1 -0.098 -
0.202 0.031 -
0.059
-
0.017 -0.258
Note: * p<0.05; ** p<0.01; *** p<0.001
DS = Disgust sensitivity; DP = Disgust propensity; SEQ = Sensory Experiences Questionnaire version 3.0; Hyper = hypersensitivity; Hypo =
hyposensitivity; SIRS = sensory interests, repetitions and seeking; EP = enhanced perception; BPQ = Body Perception Questionnaire - Very Short
Form; AQC = Alexithymia Questionnaire for Children; ID = difficulty identifying feelings; Comm = difficulty describing feelings; NEPSY =
NEuroPSYchological behavioral assessment; ToM = Theory of mind.
71
2.3.4. Partial correlations and regression analyses
For disgusting foods, based on behavioral correlations and relevance to disgust in autism,
potential covariates included gustatory sensitivity, tactile sensitivity and AQC two-factor score
for both right MI and right mOFC. Additionally, based on brain-demographics correlations,
demographic covariates include sex for right MI and IQ for right mOFC. After adjusting for
gustatory sensitivity and demographic covariates, both right MI (r = -0.335, p = 0.021) and right
mOFC (r = -0.416, p = 0.004) remain significantly correlated to DS, but not DP, across groups.
After adjusting for tactile sensitivity/AQC and demographic covariates, only right mOFC
remains correlated with DS across groups (adj. for tactile & IQ: r = -0.365, p = 0.012; adj. for
AQC & IQ: r = -0.304, p = 0.038). Regression plots for disgusting food ROIs and disgust
proneness can be found in Figure 9.
Tactile sensitivity was found to partially mediate the relationship between activity in the
right mOFC and DS across groups, but this effect did not survive after inclusion of covariates in
the model. Causal mediation analysis revealed that tactile sensitivity was a significant mediator
for the relationship between right mOFC activity and DS. The average causal mediation effect
(ACME), or the tactile sensitivity-mediated effect of DS was -0.035 (p = 0.04). However, the
average direct effect (ADE) of DS was larger and also significant (ADE = -0.176, p = 0.004),
indicating that the mediation effect of tactile sensitivity is partial. This mediating effect did not
survive after the inclusion of IQ as a covariate, which shows that the more robust ADE of DS
(after IQ adj.: β = -0.165, p = 0.012) influences activity in the right mOFC.
For disgust facial expressions, no other covariates were observed. However, given prior
results indicating that alexithymic traits can affect facial emotion recognition (Grynberg et al.,
2012), partial correlations adjusting for AQC were performed. Results indicated that the left
72
mOFC & DS (r = 0.441, p = 0.002) and right MI & DP (r = -0.288, p = 0.047) remain
significantly correlated across groups, even when controlling for AQC. Regression plots for
disgusted facial expression ROIs and disgust proneness can be found in Figure 10. All partial
correlation coefficients can be found in Table 8.
Table 8. Partial correlation coefficients for associations between the right mid-insula (MI) and
right medial orbitofrontal cortex (mOFC) with disgust sensitivity (DS) and propensity (DP)
during Disgust Food condition, adjusting for SEQ gustatory hypersensitivity, SEQ tactile
hypersensitivity, AQC 2-factor score, sex, and IQ; and the left mOFC and the right MI with
disgust sensitivity (DS) and propensity (DP) during Disgust Faces condition, adjusting for AQC
2-factor
Disgust Foods Disgust Faces
Right MI
(adj. for
SEQ
Gustatory
and sex)
Right
mOFC
(adj. for
SEQ
Gustatory
and IQ)
Right MI
(adj. for
SEQ Tactile
and sex)
Right
mOFC
(adj. for
SEQ
Tactile
and IQ)
Right MI
(adj. for
AQC and
sex)
Right
mOFC
(adj. for
AQC and
IQ)
Left
mOFC
(adj. for
AQC)
Right MI
(adj. for
AQC)
1. DS -0.335* -0.416** -0.278 -0.365* -0.124 -0.304* -0.441** -
2. DP - -0.226 - -0.226 - -0.169 - -0.288*
Note: * p<0.05; ** p<0.01
73
Figure 9. Scatter plots with regression lines across groups (left) and for each group (right) for
the Disgust Food condition of the relationship of, A. the right MI and disgust sensitivity; B. the
right mOFC and disgust sensitivity; C. the right mOFC and disgust propensity.
74
Figure 10. Scatter plots with regression lines across groups (left) and for each group (right) for
the Disgust Faces condition of the relationship of, A. the right MI and disgust propensity; B. the
left mOFC and disgust sensitivity.
75
2.4. Discussion
Despite some evidence indicating disgust processing differences in autism, such as
reduced contamination sensitivity and decreased disgusted face recognition in autism, and their
relation to activities of daily living and social engagement (Dimopoulou et al., 2006; Kalyva et
al., 2010; Yeung, 2022; Yeung et al., 2020), few studies have looked at their neural basis.
Behaviorally, we find that autistic youth show increased disgust propensity and sensitivity as
compared to the TD group, and that these traits are correlated with sensory processing and
alexithymic traits in our sample, but not with interoceptive awareness. Next, we sought to
understand the neurobiology of these differences by measuring brain activity when participants
look at images of disgusting foods (a non-social, core disgust stimulus) and disgusted facial
expressions (a social stimulus) separately. In line with our hypotheses, we found that when
viewing food-based disgusting stimuli, in the ASD group as compared to the TD group, when
viewing food-based disgusting stimuli, there was lower activity in the insula (dAI, vAI, MI) and
other emotion-related brain regions (vmPFC, mOFC). By contrast, when observing disgusted
facial expressions, we found increased activity in emotion related brain regions, including the AI,
ACC, and vmPFC/mOFC regions and reduced fusiform region activity. These activity patterns
were found to be correlated by individual differences in disgust proneness. We discuss these
findings below.
2.4.1. Between-group behavioral differences on disgust proneness and relation with other
behaviors
Based on the DPSS-R, which captures trait-level characteristics of disgust processing, we
found that autistic youth reported significantly higher levels of disgust sensitivity (DS) than their
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TD counterparts. This finding contradicts the extant literature on lower contamination sensitivity
in autistic youth, which indicates increased risk of inappropriate contamination-related behaviors
in autism (Dimopoulou et al., 2006; Kalyva et al., 2010). Youth in the ASD group also reported a
trend of higher DP than the TD group, consistent with others’ reports of an overall greater
disgust proneness trait in autism. To our knowledge, prior work has not investigated
relationships between disgust proneness and food behaviors in autism, and this remains a topic of
future work. While this is not the first study to employ the DPSS-R in autistic participants (Jager
et al., 2020; a study on misophonia), it is the first to identify disgust proneness trait differences
between autistic and non-autistic participants.
DS was significantly and positively correlated with tactile sensitivity and alexithymic
traits across groups. The association with tactile sensitivity seems to be driven by the TD group,
where the relationship is significant. This relationship is in line with prior literature showing that
tactile sensitivity can bias food selectivity and influence feelings of disgust (Cermak et al., 2010;
Chow et al., 2022; Schmitt et al., 2008). However, contrary to expectations, in the ASD group,
tactile sensitivity was not significantly correlated with disgust proneness. Rather, in autism,
disgust sensitivity was significantly and positively correlated with alexithymia and showed a
positive trend with interoceptive awareness (BPQ; Vicario, 2013).
We note that previous autism studies have highlighted discrepancies between self-reports
of interoceptive awareness in autism (Suzman et al., 2021), as they are impacted by ability to
internally time events, such as timing their heartbeats to a visual cue in which non-autistic
participants were found to perform four times better than autistic participants (Noel et al., 2018).
Similarly, alexithymia affects the identification and communication of feelings, like disgust, and
77
its relationship with DS might suggest a response bias on disgust measures in participants with
stronger alexithymic traits (Hogeveen & Grafman, 2021; Mul et al., 2018).
2.4.2. Decreased emotion-related brain activity in the ASD group for Physical disgust
(Disgusting foods)
When looking at images of disgusting foods, the ASD group showed lower activity than
the TD group in several hypothesized emotion-related brain regions (vmPFC, mOFC, ventral
anterior insula [AI], dorsal AI, mid-insula [MI] and amygdala), and higher activity in the
posterior insula (PI). These findings are supported by previous research similar neural patterns to
disgust stimuli, especially in the insula, during physical disgust contexts (Vicario, 2013; Vicario,
Rafal, Martino, et al., 2017; Wicker et al., 2003). This hypoactivity in the insula and related
regions during physical disgust may reflect disgust processing differences seen in the ASD
group.
In autism, we also found hypoactivity in the right mOFC during physical disgust. The
right mOFC has previously been implicated in processing negative emotions (Huppert et al.,
2004; Palomero-Gallagher & Amunts, 2022), which may help explain potential difficulties
autistic individuals have in evaluating contaminants. Such difficulties may lead to irregular food
behaviors and/or pica (Fields et al., 2021). Thus, our findings suggest that autistic youth have
diminished activity in physical disgust processing regions, which may affect their behavior
towards contaminants and eating behaviors (Kalyva et al., 2010; Watkins et al., 2016).
Furthermore, the observed functional activations in these emotion-related regions during the
observation of disgusting foods may represent somatosensory states for feelings of disgust which
are expressed in the autistic and non-autistic groups differently.
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Previous research in non-autistic participants has shown that lower disgust responses in
the insula may be related to a greater high-caloric food intake, which could lead to obesity and
other health problems (Houben & Havermans, 2012; Watkins et al., 2016). Autistic children
have a higher risk of obesity than do their non-autistic peers (Matheson & Douglas, 2017).
Among the risk factors for obesity in autism is selective overeating in the absence of hunger,
especially when they have high-calorie diets (Nadeau et al., 2022). Thus, our findings of reduced
insular activity may give some insight into previous literature indicating a neurobiological
mechanism whereby lower disgust processing predisposes autistic youth to prefer high-calorie
diets, eventually leading to health problems associated with obesity (Matheson & Douglas, 2017;
Nadeau et al., 2022). Future studies should examine low and high BMI when studying eating
behaviors and disgust, to investigate the relationship between BMI and insula activity (Spinelli et
al., 2021).
Correlations with disgust proneness. Further supporting the view that feelings of disgust
modulate activity in the insula and mOFC, we found that across groups, when viewing disgusting
foods, activity of the right MI significantly increased as disgust sensitivity decreased, a
relationship which seemed to be driven by the TD group. A similar pattern was found for the
right mOFC, though with both disgust sensitivity and proneness across groups, both relationships
being more strongly driven by the ASD group . Thus, this may suggest that as sensitivities
become more normative (decrease in disgust sensitivity/proneness), so may brain activity
(increase in neural activity). Furthermore, while activity in the right mOFC was partially
mediated by tactile sensitivity, which is often associated with food selectivity, the direct effects
of DS on the activations were still more robust. This indicates that tactile sensitivity influences
physical disgust behaviors, but to a smaller degree than DS, which tracks with prior research
79
showing tactile sensitivity biasing both food selectivity and disgust feelings (Cermak et al., 2010;
Chow et al., 2022; Schmitt et al., 2008).
Alternatively, diminished activity in the MI in the ASD group may indicate difficulties
with processing olfactory stimuli adequately, similar to hyposmia (reduced sensitivity to smells).
In turn, somatic markers for food-related disgust (mediated by olfaction) would be affected in
individuals presenting olfaction difficulties (Chapman & Anderson, 2012; Poppa & Bechara,
2018). In one study, it was found that males with hyposmia tend to rate higher disgust proneness
and sensitivity despite their deficient olfactory sensitivity when compared to females with
hyposmia, indicating a sex-specific difference in which hyposmic males tend to be more
inefficient in their compensatory mechanisms (Ille et al., 2017). Similarly, our study revealed a
negative association between disgust proneness and right MI activity and that males across both
groups exhibited reduced activity within the right MI compared to females. This suggests
common neural substrates for olfactory and disgust sensitivity are exhibited when observing
food-related disgusting stimuli because of somatosensory markers of disgusting, odorous food
experiences. While the aforementioned study was not conducted in autistic participants, this
relationship poses one explanation for the observed downward trend between the right MI and
disgust sensitivity in our sample, especially given the large percentage of males in our ASD
sample.
2.4.3. Activity in the ASD group for Social disgust (Disgusted facial expressions)
Overall, observing disgusted facial expressions activated the same neural regions as
observing disgusting food (with the addition of anterior cingulate [ACC]). Comparing between
groups when observing disgusted facial expressions, as predicted, the ASD group as compared to
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the TD group showed increased neural activity in the emotion-related regions (bilateral dorsal
AI, the right ventral AI, bilateral ACC, the left mOFC, and the right vmPFC), and decreased
activity in the right MI, right PI, and right amygdala, as well as the right fusiform region
important for face processing (Ashwin et al., 2007; Bastiaansen et al., 2011; Dapretto et al.,
2006; Greimel et al., 2010; Hubl et al., 2003; Kilroy et al., 2021; Monk et al., 2010; Nomi &
Uddin, 2015b; Patriquin et al., 2016; Pelphrey et al., 2007; Pierce & Redcay, 2008; Piggot et al.,
2004; Wang et al., 2004). As predicted, hypoactivity in the fusiform regions was seen in the ASD
group when observing disgusted faces (Nomi & Uddin, 2015a; Pierce & Redcay, 2008).
Interestingly, while most of these activations were not estimated by individual disgust traits, we
found the disgust-proneness-driven activity was most associated with the right MI, a region with
reduced activity in the ASD group, and the left mOFC, where ASD had greater activity.
The left mOFC is traditionally linked with the processing of positive emotions, while the
right mOFC is linked with negative emotions (Palomero-Gallagher & Amunts, 2022). Similar to
mOFC, the left and right MI have some lateralization in emotional processing, with the left MI
indicated to be more strongly solely involved with positive emotions and the bilateral MI
involved with negative emotions (Duerden et al., 2013). It is possible that our finding of
increased left mOFC activity and reduced insular activity in the ASD group when viewing a
negative emotion (disgust) may indicate differences, as seen in predictive models of FER in
autism (Takahashi et al., 2021), in emotional face categorization of the disgusted expressions in
autism. Such misevaluation of social disgust stimuli could have adverse implications on the
social relationships of autistic children as well as for vicarious learning of potentially harmful
disgusting stimuli (Liu et al., 2019). Furthermore, we find reduced activity in both the right MI
and right fusiform areas, both of which are important towards proper FER (J. S. Anderson,
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Druzgal, et al., 2011; Corbett et al., 2009; Leung et al., 2014) which bolsters our hypothesis of
reduced social vicarious disgust processing in autistic youth.
Relationship with disgust proneness. When viewing disgusted facial expressions, across
groups, the left mOFC was negatively associated with DS, while the right MI was negatively
associated with DP. These robust effects persisted after accounting for other behavioral
covariates that potentially affect FER, like alexithymic traits (Cook et al., 2013; Farhoumandi et
al., 2021; Ola & Gullon-Scott, 2020). The MI activity is influenced by disgust proneness in both
TD and ASD groups during social disgust. To the best of our knowledge, this study is the first to
show activity differences associated with disgust proneness in the MI in autistic youth, while
previous studies have shown differences within the mOFC/vmPFC in autism (Vicario, Rafal,
Martino, et al., 2017).
Typically, in an excitatory neurobehavioral system, one would expect a positive
relationship between brain activity and behavioral variables. However, we find negative
correlations in both physical and social contexts. This potentially suggests a functional
dissociation between observed brain activity and self-reported disgust proneness scores (Watkins
et al., 2016). Since the negative trend mostly persists in each group separately, the observed
relationship of brain activity and disgust proneness may reflect a potentially confounded
relationship. For instance, alexithymic traits may serve as a confound as they were significantly
and positively associated with both disgust proneness traits across groups (see Limitations).
Alexithymia also has been associated with higher BMI and obesity, indicating that alexithymic
traits may affect emotion regulation of disgust which in turn affects adequate disgust responses
to high-caloric foods in autism (Casagrande et al., 2020; Nadeau et al., 2022). Further
82
investigation in larger samples would help identify a potential moderating effect of alexithymia
on the relationship between insular activity and disgust proneness.
2.4.5. Regions showing differences in ASD for both Physical and Social disgust
Right mid-insular (MI) cortex. A common finding across our social and physical disgust
stimuli is that the right MI has reduced activity in the ASD group. The MI, which lies posterior
to the vAI and just below the PI, is most commonly associated with chemosensory processing
(for a review, see Uddin et al., 2017), particularly processing the affective value of
olfactogustatory stimuli (Pritchard et al., 1999; Stevenson et al., 2015). Additionally, in humans,
chemosensation of spoiled food and the sensory affect of distaste are the phylogenetic roots for
the primary emotion of disgust (Toronchuk & Ellis, 2007; Zheng et al., 2018). Hence, it seems
fitting that the MI is correlated with disgust proneness and shows reduced activity in ASD. To
the best of our knowledge, this study is the first to show activity differences for disgust
processing in the MI in autistic youth.
Further, we find increased activity in the MI is related to decreased disgust proneness in
both TD and ASD groups during social disgust, but only in the TD group during physical
disgust. The finding of a negative correlation between brain activity and disgust proneness is
contradictory to our hypothesis, as we initially predicted increased activity when experiencing
increased disgust, in line with prior studies showing increased activity in insular regions with
increased sensory sensitivities (S. A. Green et al., 2015). Further studies are needed to better
understand this effect and whether it may potentially be related to measurement issues in selfreports in autism (Gunderson et al., 2023), or other mediating factors. Further investigation in
83
larger samples would help identify potential moderating effects on the relationship between
insular activity and disgust proneness.
The MI also acts as an information processing link between the ventral anterior and
posterior cortices of the insula (Cerliani et al., 2012; Cloutman et al., 2012; Uddin et al., 2017).
While the MI is focused olfaction and gustation, the vAI is an emotion processing region and
more commonly associated with disgust (Overton et al., 2021; Vicario, Rafal, Martino, et al.,
2017; Woolley et al., 2015), and the PI is associated with processing interoceptive information
and visceral sensations (Craig, 2003; Holzer, 2017; Uddin et al., 2017). The AI and MI are also
both associated with processing feelings of nausea (Stephani et al., 2011). Although, typically,
the ventral AI has been implicated with processing disgust-related emotional information, there
is debate over whether the vAI is involved in general emotion processing (L. F. Barrett et al.,
2006; Cauda et al., 2012; Chang et al., 2013; Menon & Uddin, 2010; Molnar-Szakacs & Uddin,
2022; Nomi et al., 2019; Uddin et al., 2017). Our findings may suggest that right MI is more
attuned with disgust-specific processing, while the vAI may be more heavily involved in
processing general emotional information. However, such a conclusion is beyond the scope of
this study. Another study suggests the existence of valence gradient in the insula for processing
disgusting food stimuli, where the appraisal of foods occurs in the anterior insular (dAI, vAI)
regions and contaminant appraisal occurs in the posterior (MI, PI) regions (Watkins et al., 2016).
Alternatively, we hypothesize that due to its functional nature and its connections to other insular
regions, the MI may serve as a unique hub for processing unpleasantness or repulsion (related to
the DS and DP traits) experienced through felt or vicarious disgust, by receiving interoceptive
feedback from the PI and negative emotional appraisal inputs from the vAI and relaying
information to the dAI for downstream decision-making. According to this hypothesis, the
84
reduced MI activity in both disgust contexts in autistic youth shows core disgust processing
difficulties that may affect responses to disgust stimuli. However, this hypothesis is beyond the
scope of these results, but future connectivity studies could help delineate the information flow
within the insula during physical and social disgust contexts. To our knowledge, this is the first
reported incidence of disgust-related activation differences in the MI in autistic youth.
Additionally, two distinct regions of the right MI were seen to be active for the observation of
disgusting foods and disgusted faces respectively, indicating separate functionally specialized
areas of the MI that drive context-specific disgust processing (Vicario, Rafal, Martino, et al.,
2017).
Medial orbitofrontal cortex (mOFC). Another region that we find associated with disgust
proneness during both physical and vicarious disgust is the mOFC, a region previously shown to
be involved in bringing together somatic emotion information (including disgust) with decision
making (Somatic marker hypothesis; Bechara, 2013; Damasio, 2011). The bilateral mOFC was
shown to be differentially activated during our study, with decreased activity in ASD during
observation of disgusting foods and increased activity when observing disgusted faces. In both
physical and social disgust contexts, the mOFC activity was quite robust and seemed to be driven
by disgust sensitivity above and beyond sensory and alexithymic individual differences. As
previously mentioned, the mOFC is a core emotion-related brain region responsible for
triggering somatosensory states associated with physical and social disgust past experiences
(Bechara, 2013; Bechara & Damasio, 2005). Thus, activity within the mOFC may potentially be
driven by other interoception, alexithymia or somatosensory information. Tactile sensitivity
plays a significant role in disgust and food behaviors in autistic children (Chow et al., 2022;
Schmitt et al., 2008). Thus, this may explain the observed partially mediating effect of tactile
85
sensitivity on the relationship between right mOFC activity during disgusting food observation
and DS. However, for both disgust contexts, we found that mOFC activity was driven primarily
by DS, irrespective of the covariates in the model, indicating a prominent role in difficulties with
the disgust processing network in autistic youth. The amygdala and mOFC are important parts of
the limbic system and face processing systems (Fusar-Poli et al., 2009). While amygdala activity
is reduced in ASD, we find increased left mOFC activity in ASD during the observation of
disgust faces. Amygdala activity results are concordant with previous literature exploring
emotional face processing in autism (Ashwin et al., 2007). Contrary to extant FER literature, we
observed higher mOFC activity during viewing of disgusted facial expressions. Given that
autistic youth experience attentional biases (initially autistic youth are hypervigilant of disgusted
faces, then later show avoidance to faces) when viewing emotion faces (Zhao et al., 2016), we
attribute this increased activity in limbic regions (except amygdala) to hypervigilance to faces in
autism. While bilateral activations were observed, the lateralization of significant DS-mediated
activity in the two disgust contexts suggests a pattern of context-specific behavior.
2.4.6. Limitations
In the current study, we utilize several self report measures: DPSS-R and DES-C for
disgust proneness, AQC for alexithymia, and BPQ for interoceptive awareness. About 50% of
autistic individuals have alexithymia (Cuve et al., 2022), which may affect self-reports of disgust
proneness. Similar self-report reliability issues have been identified in the BPQ (C. D. Butera et
al., 2023; Garfinkel et al., 2016). Thus, further investigation into the effect of alexithymia on
self-reports of emotional traits is required to determine their reliability in individuals with such
emotion identification and communication difficulties. Other limitations of this study include the
86
small ROI cluster sizes, and the small sample sizes which are suitable for whole-brain
comparisons but insufficient for error-free brain-behavior statistical analyses (Marek et al.,
2022). Additionally, autism is a heterogeneous neurodevelopmental condition (Masi et al., 2017)
and our study was limited to only high-functioning right-handed individuals with autism and our
sample was biased towards males. Further studies are needed with larger heterogeneous sample
sizes (n>2000; Marek et al., 2022), which would help in better understanding the relationship of
disgust proneness traits with the differential activity of the disgust processing network regions,
with more diversity in terms of IQ and sex.
2.5. Conclusion
In this study, we explored the neural substrates underlying disgust processing in autism
and differences in the disgust networks in ASD and TD groups when observing disgusting foods,
and disgusted facial expressions. Behaviorally, we found, to our knowledge for the first time,
that the autism group showed increased disgust proneness compared to the TD group.
Neuronally, also for the first time, we found that in both disgust contexts, relatively the same
emotion-related brain regions were involved in physical and social disgust processing
respectively. Importantly, in general, the ASD group showed decreased processing in these
regions during physical disgust, and increased processing during social disgust and neural
patterns in parts of the insula and bilateral medial orbitofrontal cortex were related to disgust
proneness, beyond other factors (alexithymia, and tactile and gustatory sensory sensitivity), such
that activity within the right mid-insula and the bilateral medial orbitofrontal cortex was lower
with higher traits of disgust sensitivity and propensity.
87
These neural and behavioral differences shed light on the neurobiological basis of
potential food-related and social difficulties experienced by autistic youth. Further, differences in
processing other’s facial expressions of disgust may lead to difficulty learning about potential
contaminants and dangers from other’s experiences (vicarious disgust learning). How the activity
differences in the insula and orbitofrontal areas contribute towards differences in vicarious
disgust learning, eating differences, and contamination behavior differences in autistic youth –
all of which may be indirectly linked with other secondary conditions, like obesity or pica –
remains a topic of future research. Such disgust behavioral differences may also affect social and
moral domains of their life (see Chapter 1), possibly worsening the social difficulties faced by
autistic youth. Greater understanding of how disgust is perceived differently and its influence on
behavior in autistic youth provides valuable information that could be used in the development of
targeted social and eating interventions for the autistic population. Future research may help to
identify the connectivity differences within the disgust networks in TD and ASD groups, which
might help identify dysfunctional pathways or potential compensatory network connections.
88
CHAPTER 3: FUNCTIONAL CONNECTIVITY DIFFERENCES DURING PHYSICAL
AND SOCIAL DISGUST PROCESSING IN AUTISM
Abstract
This study investigates alterations in functional connectivity between the mid-insula (MI)
and medial orbitofrontal cortex (mOFC) with other brain regions during both social and physical
disgust processing in individuals with autism spectrum disorder (ASD). The significance of this
research lies in uncovering the intricate neural connections involved in disgust processing in
ASD, shedding light on potential mechanisms contributing to socio-emotional challenges and
sensory sensitivities observed in this neurodevelopmental condition. Here, we compare the
functional connectivity differences between autistic and typically-developing (TD) youth during
the processing of physical and social disgust. Utilizing psychophysiological interaction (PPI)
analysis on functional MRI data from 49 participants (25 ASD, 24 TD), we identified intriguing
connectivity patterns. Specifically, we observed significant hypoconnectivity with the dorsal
anterior insula (dAI) and left ventromedial prefrontal cortex (vmPFC) in the ASD group
compared to the TD group. Conversely, we found significant hyperconnectivity with regions
such as the amygdala, orbitofrontal cortex (OFC), and putamen in the ASD group compared to
the TD group. Notably, the hypoconnectivity between the right mid-insula (MI) and left dAI in
the ASD group while observing disgusting foods and the hyperconnectivity of the right MI with
the right amygdala in the ASD group while processing disgust facial expressions were
significantly correlated with disgust proneness traits. These findings provide valuable insights
into the neural mechanisms underlying disgust processing in autistic youth and offer potential
avenues for future research and clinical intervention.
89
3.1. Introduction
Autism spectrum disorder (ASD) is a heterogeneous, neurodevelopmental condition,
characterized by difficulties in social cognition, restricted and repetitive interests and behaviors,
and sensory sensitivities (American Psychiatric Association, 2013). Some socio-emotional
differences may arise particularly from the emotion of disgust, with increased disgust proneness
found in autism (Jayashankar & Aziz-Zadeh, 2023). Differences in disgust processing among
autistic children may lead to pica behaviors, gastrointestinal disturbances, and socio-emotional
challenges, including difficulties in social communication and vicarious learning of disgust
(Dimopoulou et al., 2006; Madra et al., 2020). Young individuals with autism spectrum disorder
(ASD) may exhibit lower disgust proneness, indicating individual-specific traits related to
disgust (Kalyva et al., 2010). About 23% of these individuals engage in pica behaviors,
consuming items not intended for consumption (Fields et al., 2021). Despite these observations,
the literature on disgust experiences in ASD is notably limited, primarily due to researchers often
studying disgust alongside other basic emotions and general differences in emotion and sensory
processing (Harms et al., 2010; Kalyva et al., 2010; Vicario, Rafal, Martino, et al., 2017; Zhao et
al., 2016).
Additionally, between 53% and 94% of individuals with autism deal with sensory
sensitivities, which significantly contribute to heightened feelings of disgust (Kirby et al., 2022).
Olfactory and gustatory sensitivities, particularly in how food odors are perceived as pleasurable
or unpleasant, play a crucial role in shaping food behaviors in autistic children, with unpleasant
odors leading to strong feelings of disgust. Individual olfactory sensitivity notably influences the
reluctance of autistic children to try new foods, a phenomenon not observed in the comparison
group (Chistol et al., 2018; Luisier et al., 2015; Stafford et al., 2017). While olfactory and
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gustatory sensitivities are recognized factors, the precise link to higher disgust proneness remains
an open question. On a different note, tactile sensitivities are associated with heightened disgust
toward certain food items, and traits related to textural sensitivity predict eating patterns and
pickiness in autistic children, with a noticeable aversion to mushy, soft textures and a preference
for crunchy, hard textures (Baraskewich et al., 2021; Coulthard et al., 2022; Martins & Pliner,
2006; Schmitt et al., 2008). Given the numerous food selectivity issues in autism, further studies
are essential to understand how disgust processing may mediate or be influenced by these
factors. Additionally, autism often co-occurs with alexithymia, marked by difficulties in
identifying emotions, correlating with differences in disgust processing, possibly due to
heightened alexithymic traits focusing on interoceptive processes (Cuve et al., 2022; Scarpazza
et al., 2015; Sifneos, 1973), and also, autistic youth experience difficulties with intention
understanding that may affect how they perceive disgust stimuli, such as disgusted faces
(Andreou & Skrimpa, 2020). This intricate interplay requires further exploration to enhance our
understanding of the complex dynamics involving sensory sensitivities, alexithymia, and disgust
processing in autism.
Our prior research found these disgust processing differences were related to differences
in neural processing within the insula (Jayashankar et al., under review). Specifically, when
looking at disgusting foods, autistic youth, as compared to typically developing controls (TD),
had significantly less activity within the right mid-insula (MI) and the right medial orbitofrontal
cortex (mOFC). Further, when viewing disgust facial expressions, there was significantly less
activity in the right MI and increased activity in the left mOFC in the autism group. This activity
was correlated with disgust proneness (individual-specific disgust traits, consisting of disgust
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sensitivity [DS] and disgust propensity [DP]; van Overveld et al., 2006) in the ASD group
(Jayashankar et al., under review).
While this study showed differences in neural activity in autism during disgust
processing, there remains the question of how connectivity between different brain regions and
networks may contribute to differences observed in ASD. Indeed, differences in neural
connectivity have been strongly linked to autism, particularly for socio-emotional processing
(Bai et al., 2023; Dell’Osso et al., 2023; Ilioska et al., 2023; Nomi & Uddin, 2015a; Pitskel et al.,
2011, 2014). In particular, aberrant functional connectivity with the anterior insula (AI) has been
implicated in autism (Caria & de Falco, 2015; Ebisch et al., 2011; Molnar-Szakacs & Uddin,
2022; Nomi et al., 2019).
In neurotypical adults, neuroimaging data suggests that variations in the functional
integrity and connectivity of the AI can lead to differing experiences of both core and vicarious
disgust (Lamm & Singer, 2010; Sarinopoulos et al., 2010; Shoemaker, 2012). In fact,
connectivity between the insula and other neural networks and brain regions has strongly been
linked to disgust processing (Jayashankar, Kilroy, et al., 2022; Uddin et al., 2017; Vicario, Rafal,
Martino, et al., 2017; Wicker et al., 2003). Prior studies indicate that individual differences in AI
functional connectivity may lead to differences in disgust experiences (Pitskel et al., 2014), both
for core and vicarious disgust (Shoemaker, 2012). Furthermore, they may impact trait-level
disgust proneness, educational environments, and socio-cultural factors related to disgust (V.
Curtis, 2011; Davey, 2011; Vicario, Rafal, Borgomaneri, et al., 2017). In autism, studies have
shown that aberrant AI functional connectivity is related to difficulties in emotion, empathy, and
social processing, including disgust emotion-related processing (J. S. Anderson, Druzgal, et al.,
2011; Anderson, Nielsen, et al., 2011; Bird et al., 2010; Caria & de Falco, 2015; Ebisch et al.,
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2011). Furthermore, cognitive reappraisal studies indicate that autistic individuals exhibit
decreased amygdala-ventrolateral prefrontal cortex functional connectivity (associated with
cognitive control) and fail to modulate AI activity when triggered by disgust experiences (Pitskel
et al., 2014). Taken together, there is evidence that AI functional connectivity may be aberant in
autism, and impact disgust-related processing (as well as other functions).
The insula is a complicated structure, with connections to many networks (Couto et al.,
2013; Kirby & Robinson, 2017), and is involved in different social (Haxby et al., 2000; Hubl et
al., 2003), sensory (Damasio, 2003, 2008; Damasio et al., 2000), emotional (L. F. Barrett et al.,
2006; Cauda et al., 2011; Chang et al., 2013; Menon & Uddin, 2010), and cognitive tasks (Nestor
et al., 2003; Ogar et al., 2006). Previous research also suggests that disgust may trigger many
regions between the insula and frontotemporal regions related to emotional, socio-cognitive, and
interoceptive processing (Adolfi et al., 2017). The AI, in part, may also process domain-specific
disgust experiences, much like as a disgust experiences ‘hub’, managing information from
emotion-related and social cognition regions (amygdala, striatum, putamen, medial prefrontal,
orbitofrontal, sensory, and anterior cingulate cortices) and the viscera (Vicario, Rafal, Martino, et
al., 2017). How connectivity between these regions/networks differ in autism, specifically with
regard to disgust processing, remains to be better understood.
Besides the AI, there may be other significant brain regions associated with social disgust
processing, including areas related to facial expression processing (Vicario, Rafal, Borgomaneri,
et al., 2017), such as the anterior cingulate cortex (ACC), precuneus, fusiform gyrus, inferior
frontal gyrus (IFG), and amygdala (Ashwin et al., 2007; Dapretto et al., 2006; Greimel et al.,
2010; Hubl et al., 2003; Kilroy et al., 2021; Monk et al., 2010; Nomi & Uddin, 2015; Patriquin et
al., 2016; Pelphrey et al., 2007; Pierce & Redcay, 2008; Piggot et al., 2004; Wang et al., 2004).
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These regions could exhibit distinct activation patterns in facial expression recognition (FER) in
individuals with autism, influenced by developmental factors (Bastiaansen et al., 2011). Social
disgust is characterized by the ability to recognize another person's disgusted facial expression
and vicariously experience feelings of disgust (Askew et al., 2014; Reynolds & Askew, 2019).
This involvement is reflected in the activation of brain regions like the inferior occipital regions,
fusiform face area (FFA), and the posterior superior temporal sulcus (pSTS) during disgusted
face processing, along with limbic regions like the amygdala (Haxby et al., 2000; Hubl et al.,
2003). However, most studies have focused on various facial expressions and have not
specifically examined disgusted facial expressions, making it challenging to infer specific
neuronal differences related to disgust in the context of facial expression recognition in
individuals with autism.
3.1.1. Present study
Here, we investigate changes in functional connectivity between the insula and mOFC
and other brain regions during both social (looking at facial expressions of disgust) and physical
(looking at pictures of rotten foods) disgust processing. In line with prior work (Jayashankar et
al., under review), we predict that there will be reductions in the neural connectivity between
disgust emotion-related regions (insula, prefrontal and orbitofrontal cortices, amygdala, basal
ganglia). We further predict that connectivity patterns will be modulated by individual
differences in disgust proneness.
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3.2. Methods
3.2.1. Participants
Participants between the ages of 8-17 (mean) years were recruited from clinics in the
greater Los Angeles area, through local schools, social media advertising, and with the help of
the Simons Foundation Autism Research Initiative (SFARI) Research Match. This age range was
selected to specifically look at the brain during development, while selecting a lower bound that
could meet the scanning environment and a broad age range to meet our recruitment goals. We
use ‘youth’ to refer to children and adolescents in our participant sample.
Our study protocol was approved by the University of Southern California (USC)
Institutional Review Board (IRB) in compliance with the Declaration of Helsinki. Written
informed consent and assent (for minors) was obtained from all parent(s) and participants
respectively prior to data collection.
Autistic participants (ASD; n=25, 5 female)
Inclusion criteria for autistic youth included: (a) a clinical diagnosis of autism spectrum
disorder, confirmed by the Autism Diagnostic Observation Schedule-2 (C. Lord et al., 2012) and
Autism Diagnostic Interview - Revised (C. Lord et al., 1994); (b) IQ≥80 assessed by Wechsler
Abbreviated Scale of Intelligence, 2nd Edition (WASI-II, Wechsler, 2011); (c) no prior or
concurrent diagnosis of other major neurological, psychiatric, or developmental disorders (e.g.,
schizophrenia, brain tumor, and epilepsy) mentioned during pre-screening procedures; (d) no
known structural brain abnormalities (e.g., aneurysm) upon review of T2 scans by an in-house
neuroradiologist; (e) right-handedness, determined by a modified Oldfield questionnaire
(Oldfield, 1971); and (f) English-speaking youth and parents, as we used standardized measures
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in English only. The IQ cut-off of ≥80 ensured that participants could understand task directions,
and to match ASD and TD groups for IQ.
Age- and sex-matched controls (TD; n=24, 12 female)
Inclusion criteria included: (a) no ADHD as identified by the Conners 3rd edition parent
screening questionnaire (Conners et al., 2011); (b) no first-degree relatives with ASD and no
current or previous concerns of ASD diagnosis; and the (c)-(f) criteria for the ASD group.
3.2.2. Behavioral measures
Individual Differences in Disgust Processing. Trait levels of disgust feelings were
assessed by the Disgust Propensity and Sensitivity Scale – Revised Child version (DPSS-R;
Georgiadis et al., 2020; Olatunji et al., 2007), a self-report questionnaire designed to measure
traits of disgust proneness, consisting of: (a) the frequency of disgust experiences (disgust
propensity, or DP); and (b) the negative emotional impact of disgust stimuli (disgust sensitivity,
or DS). This measure consists of 15 items scored on a five-point Likert scale (Cavanagh &
Davey, 2000; Olatunji et al., 2007). All subscales had test-retest reliability and high internal
consistency (DP ICC = 0.69, DS ICC = 0.77; van Overveld et al., 2006), and moderate
convergent validity with both the Disgust Scale (Haidt et al., 1994) and Disgust and
Contamination Sensitivity Questionnaire (Rozin et al., 1984).
Sensory Sensitivities. Individual differences in sensory sensitivities were assessed by the
Sensory Experiences Questionnaire Version 3.0 (SEQ; Ausderau et al., 2014; Baranek, 2009;
Baranek et al., 2006), a 105-item parent report measure, scored on a five-point Likert scale. It
measures four sensory response patterns (hypersensitivity; hyposensitivity; sensory interests,
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repetitions and seeking [SIRS]; enhanced perception), five modality-specific sensory sensitivities
to regular, naturally-occurring sensory stimuli (gustatory, tactile, visual, auditory, vestibular),
and two sensory experience contexts (social & non-social). Factor analysis revealed that the SEQ
structure has a reasonable fit, with strong (>0.2) and significant (p<0.001) factor loadings.
Additional Measures: Interoception, Alexithymia, Theory of Mind (ToM). Interoceptive
awareness, alexithymic traits, and ToM individual differences were measured using the Body
Perception Questionnaire Very Short Form (BPQ-VSF; Cabrera et al., 2018; Porges, 1993), the
Alexithymia Questionnaire for Children (AQC; Rieffe et al., 2006), and the ToM total score of
the NEPSY-II (Korkman et al., 2007) respectively. The BPQ-VSF is a 12-item child report with
high test–retest reliability (ICC= 0.97) and with good internal consistency in an American
sample (ω = 0.91; Cabrera et al., 2018). The AQC, which has been adapted from the Toronto
Alexithymia Scale, is a three-point Likert scale with three subscales: difficulty identifying
feelings (AQC ID), difficulty describing feelings (AQC Comm), and externally-oriented thinking
(AQC EOT) with internal consistencies (ɑ) of 0.73, 0.75 and 0.29 respectively. The AQC EOT
subscale was excluded due to its low consistency, and a sum total score of the two other
subscales (AQC 2-factor) was calculated (C. D. Butera et al., 2023; Loas et al., 2017). The ToM
behavioral assessment is part of the Social Perception domain of the NEPSY with decent internal
reliability (r ≥ 0.8) and test–retest reliability ICC ≥ 0.5 in ages 7–16 years.
3.2.3. MRI data acquisition protocol
All data were acquired with a 3-Tesla Siemens MAGNETOM Prisma System (Siemens
Medical Solutions, Erlangen, Germany) using a 20-channel head coil. Functional echo planar
imaging (EPI) volumes were acquired continuously with the following parameters: TR=2s,
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TE=25ms, flip angle=90°, FOV 64 x 64 matrix, in-plane resolution 3x3mm, and 41 transverse
slices, each 3mm thick, covering the whole brain. We also acquired a structural T1-weighted
MPRAGE in each subject (TR=2.53s, TE=3.09ms, flip angle=10°, FOV 256 x 256 matrix, 208
coronal slices, 1mm isotropic resolution). Spin-echo EPI field maps were acquired in A-P and PA directions with identical geometry to the functional data for EPI off-resonance distortion
correction (TR=1.02s, TE1=10ms, TE2=12.46ms, flip angle=90°, voxel size=3mm isotropic,
FOV 224 x 224 x 191 mm3 matrix).
3.2.4. fMRI task
Stimuli. Task stimuli consisted of four categories: neutral foods, disgusting foods (e.g.,
rotten meat), neutral expression faces, and disgusted expression faces (Figure 3). All stimuli
were overlaid on a white background, following methodology in previous studies (Vicario,
Rafal, Borgomaneri, et al., 2017; Vicario, Rafal, Martino, et al., 2017; Wicker et al., 2003). The
neutral and the disgusted facial expressions were chosen from an online repository (NimStim;
Tottenham et al., 2009) and from previous research (‘EmStim’; Kilroy et al., 2021), then edited
and counterbalanced so that each participant saw the same actor depicted displaying a neutral
and disgusted facial expression. For each participant, 18 images were used from each stimulus
category. Additionally, to ensure that the neutral food images were indeed items the participant
truly had no preferential or disgusting feelings for, all participants were administered a
questionnaire prior to participating in the study, asking them their preferences for each neutral
food pictured in the stimuli. Only neutral stimuli specific to a participant were included in the
“neutral food” category.
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fMRI Task. One fMRI run was presented to all participants, six blocks per stimulus
category (disgusting foods, neutral foods, disgusting facial expressions, neutral facial
expressions). Within each 15-second block, three different images from the same category were
presented with a 250-millisecond fixation crosshair between each stimulus (e.g., three different
disgusting food images). Thus, the fMRI task consisted of 24 blocks (5 per stimulus category),
lasting for a single 10-minute run. Prior to scanning, all participants completed a mock scanning
session to familiarize them with the scanning environment and to help reduce head motion
artifacts.
3.2.5. fMRI data analysis
Similar to previous research (Jayashankar et al., under review), we expected ~10% of
ASD participants to exhibit high head motion (Kilroy et al., 2021). Participants who exhibited
extreme in-scanner head motion (absolute head motion > 1.5 mm and relative head motion > 0.3
mm) were excluded from data analysis. No significant differences in absolute (t = -0.548, p =
0.293) and relative (t = -0.952, p = 0.173) head motion were found between the two groups after
exclusion of high head motion subjects (excluded n = 3; not included in participant counts). All
analyses followed best practices in fMRI analysis, as detailed in our prior studies (Kilroy et al.,
2021). The data analytic approach used to address each of our research questions utilized
FMRIB’s Software Library 6.0 (FSL; Jenkinson et al., 2002, 2012; Jenkinson & Smith, 2001;
Smith et al., 2004; Woolrich et al., 2004) and entailed: (i) Within-Subject
Analyses/Preprocessing; (ii) Within-Group/First-level connectivity analysis; and (iii) BetweenGroup connectivity comparative analysis.
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a. Within-subjects preprocessing
Standard preprocessing pipeline was performed involving: (a) structural T1 brain
extraction and non-brain tissue removal; (b) smoothing with 5 mm FWHM Gaussian kernel; (c)
B0 unwarping along y-axis; (d) high pass filter with 100 sec cutoff; (e) realignment using
MCFLIRT to obtain motion estimates; (f) Independent component analysis (ICA). Preprocessed
data was fed into the ICA AROMA algorithm (Pruim et al., 2015), which filtered out noise and
motion components from the whole brain signal. Registration to the MNI-152 standard atlas
using 12 degrees-of-freedom affine transformation and FNIRT nonlinear registration (Jenkinson
et al., 2002; Jenkinson & Smith, 2001) were performed.
b. Functional connectivity analyses
We performed psycho-physiological interaction (PPI; McLaren et al., 2012) analysis to
compare functional connectivity between ASD and TD groups. This analysis compares the
functional correlation of the disgust processing regions (right MI and right mOFC seeds for
disgusting foods, right MI and left mOFC seeds for disgusted faces) to the rest of the brain
during the disgust processing task. Individual seed regions were defined by previous analyses
using functional activation clusters taken from the contrast of TD and ASD groups, during the
Disgust Food>Rest and Disgust Faces>Rest conditions (Jayashankar et al., under review). The
time series was extracted from the functionally defined regions of interest, and modeled as the
physiological regressors. Seed-to-voxel activity between the seed region and the other regions in
the brain were modeled as interaction effects of the physiological regressor (seed time series)
with the condition regressor (task block design) defined during first-level analysis. First-level
analysis included modeling the stimulus conditions of interest (Disgust Food, Disgust Faces) for
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each participant as separate regressors, derived task block design to render the hemodynamic
responses, along with their respective temporal derivatives. Subject-specific head motion
parameters were added as nuisance regressors. Significantly different activity between task and
rest resulted in the PPI effect of functional connectivity.
Second-level analysis compared the groups on each of the PPI main effects for the two
task conditions. Between-group comparisons between the TD and ASD groups were performed
using higher level mixed-effects analyses with FSL's FLAME 1 algorithm. These results were
assessed for the main PPI effects of the disgust conditions (disgusting foods>rest and disgusted
faces>rest). Significance of whole-brain seed-to-voxel activity differences was tested at
threshold of Z>3.1 (equivalent to p-value less than 0.001) with a corrected cluster size
probability threshold of p<0.05. We used age, IQ and sex as covariates in the analysis.
Additionally, we performed small volume correction (SVC) analysis with a significance
threshold of p<0.05 using a mask defined by merging the large-scale meta-analyses on
Neurosynth for the search terms “disgust”, “emotional faces”, and “food”, with insula structural
parcellations from previous research (Deen et al., 2011). This mask included portions (not the
entire region) of the insular divisions (dAI, vAI, PI), dorsolateral prefrontal cortex (dlPFC),
ventromedial prefrontal cortex (vmPFC), medial orbitofrontal cortex (mOFC), anterior cingulate
cortex (ACC) , fusiform areas, amygdala, hippocampus, putamen, nucleus accumbens, lingual
gyrus, middle temporal gyrus, and superior frontal gyrus (see Supplementary Figure S1).
3.2.6. Correlational analysis
Although some studies indicate the need for large samples for correlational analysis
(Marek et al., 2022), we performed exploratory correlations within our ASD sample to observe
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the influence of individual disgust proneness traits on functional connectivity. Only ROIs with
≥80% of non-zero data were used in correlation and regression analyses to avoid strong floor
effects. All correlation analyses and visualizations were performed using R (R Core Team,
2013). Behavioral and brain activity measures were initially assessed for normality and linearity.
For all behavioral measures, we used the raw total scores, except for IQ for which age-normed
scores were used. Missing data points were corrected using rough imputation (roughfix in the
randomForest package in R), as long as no more than 15% of the data points were missing (no
measures had missing data exceeding missing threshold). Pearson correlations were performed to
assess associations between behavioral variables and brain activity in the different ROIs with
correlated activity as the seed regions. For regions with significant correlations with
demographic variables, we performed partial correlations controlling for age, sex and IQ. To
adjust for the issue of multicollinearity (see Supplementary Figure S1), only one of the collinear
variables was chosen as a proxy. For instance, only the gustatory and tactile subscales were used
in partial correlations as proxies due to their behavioral relevance to disgust.
3.3. Results
3.3.1. Functional connectivity differences during the Disgust Food condition
a. Right mid-insula (MI)
When viewing disgusting foods, the ASD group, compared to the TD group, showed
significantly lower functional connectivity between the right MI and: (1) the bilateral dorsal AI;
and (2) left vmPFC (all ps<0.05, SVC). Significantly greater connectivity in the ASD group was
found between the right MI and: (1) the bilateral ventral AI; (2) bilateral amygdala; (3) right
posterior insula (PI); (4) bilateral orbitofrontal cortices (OFC); (5) and right occipital and
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temporal fusiform cortices (all ps<0.05, SVC). No whole-brain seed-to-voxel results were
observed. A full table of results can be found in Table 9 and Figures 11A & S2A.
b. Right medial orbitofrontal cortex (mOFC)
When viewing disgusting foods, the ASD group, compared to the TD group, showed
significantly less connectivity between the right mOFC and the: (1) right dorsal and ventral AI;
(2) right amygdala; and (3) left anterior parahippocampal cortex. The ASD group showed
significantly greater functional connectivity between right mOFC and: (1) left dorsal and ventral
AI; (2) left MI; (3) left amygdala; (4) bilateral vmPFC; (5) left mOFC; (6) left pallidum; (7) left
putamen; (8) right middle temporal gyrus; (9) bilateral temporal occipital fusiform cortex; and
(10) right occipital fusiform cortex (all ps<0.05, SVC). No whole-brain seed-to-voxel results
were observed. A full list of results can be found in Table 9 and Figures 11B & S2B.
Table 9. Seed-to-voxel activations for the Disgust Food condition for the seeds: right mid-insula
(MI) and right medial orbitofrontal cortex (mOFC; p<0.05, SVC)
Seed Contrast Max Z Cluster size
(p<0.05) X Y Z Laterality Region
Right MI TD>ASD 2.3 17 -42 0 -4 L Dorsal anterior insula (dAI)
1.82 3 0 38 -28 L Ventromedial prefrontal cortex
(vmPFC)
2.09 3 40 16 -2 R dAI
ASD>TD 2.22 30 -22 -8 -14 L Amygdala
2.33 25 -30 2 -18 L Amygdala
2.66 9 -44 -54 -56 L Cerebellum Crus II
2.58 10 -38 48 -14 L Frontal pole
2.2 5 -12 -94 -12 L Occipital pole
2.66 21 -26 32 -16 L Orbitofrontal cortex (OFC)
2.09 7 -42 10 -38 L Temporal pole
2.5 66 -36 16 -10 L Ventral anterior insula (vAI)
2.17 9 30 -8 -20 R Amygdala
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2.54 5 24 62 -12 R Frontal pole
2.92 11 20 -86 -14 R Occipital fusiform
2.61 7 32 -86 -12 R Occipital fusiform
2.85 21 34 34 -20 R OFC
2.04 5 38 -12 0 R Posterior insula (PI)
2.59 12 40 -38 -22 R Temporal fusiform
1.71 3 42 -56 -20 R Temporal fusiform
2.87 138 28 18 -10 R vAI
Right mOFC TD>ASD 2.56 18 -22 -4 -34 L Anterior parahippocampal
cortex
2.96 86 26 2 -30 R Amygdala
2.64 47 40 16 2 R dAI
1.93 4 42 14 -14 R vAI
ASD>TD 2.16 8 -22 -6 -14 L Amygdala
2.3 20 -30 24 0 L dAI
2.15 11 -44 -10 2 L dAI
2.73 8 -38 46 -16 L Frontal pole
2.56 30 -34 4 -10 L Mid-insula (MI)
2.74 9 -30 24 -20 L Medial orbitofrontal cortex
(mOFC)
2.47 34 -10 -2 -2 L Pallidum
2.13 3 -20 4 -8 L Putamen
1.99 4 -42 -52 -10 L Temporal occipital fusiform
2.29 7 -48 12 -42 L Temporal pole
2.04 10 -26 14 -18 L vAI
2.12 3 -2 58 -12 L vmPFC
2.2 5 38 -82 -12 R Lateral occipital cortex
2.49 6 62 -44 10 R Middle temporal gyrus
2.04 6 20 -88 -16 R Occipital fusiform
2.64 14 42 -10 -2 R PI
2.43 11 38 -12 16 R PI
2.07 10 38 -60 -20 R Temporal occipital fusiform
2.27 14 38 18 -44 R Temporal pole
2.42 18 8 54 -8 R vmPFC
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Figure 11. Psychophysiological interaction (PPI) results for co-activations (nodes) and
connections (edges) of the right mid-insula (A) and the right medial orbitofrontal cortex (B) in
the Disgust Food condition. Hypoconnectivity in ASD and TD>ASD co-activations activations
appear in red. dAI = dorsal anterior insula; vAI = ventral anterior insula; MI = mid-insula; PI
= posterior insula; vmPFC = ventromedial prefrontal cortex; OFC = orbitofrontal cortex;
mOFC = medial OFC; Amyg = amygdala; Put = putamen; Pall = pallidum.
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3.3.2 Functional connectivity differences during the Disgust Faces condition
a. Right mid-insula (MI)
When viewing disgusted faces, the ASD group, compared to the TD group, showed
significantly reduced functional connectivity between the the right MI and the: (1) right dorsal
and ventral AI; (2) right PI; (3) left anterior cingulate cortex (ACC); (4) left vmPFC; (5) right
mOFC; (6) and left hippocampus. The ASD group showed significantly greater connectivity
between the right MI and the: (1) left MI; (2) left PI; (3) bilateral amygdala; (4) left midcingulate cortex (MCC); and (5) left anterior parahippocampal cortex (all ps<0.05, SVC). No
whole-brain seed-to-voxel results were observed. A full list of results can be found in Table 10
and Figures 12A & S3A.
b. Left medial orbitofrontal cortex (mOFC)
When viewing disgusted faces, the ASD group, compared to the TD group, showed
significantly reduced connectivity between the left mOFC and the: (1) bilateral dorsal and
ventral AI; (2) bilateral PI; (3) right ACC and MCC; (4) right vmPFC; (5) right OFC; (6) right
temporal occipital fusiform cortex; (7) left hippocampus; and (8) right superior frontal gyrus.
The ASD group showed significantly more connectivity between the left mOFC and the: (1) left
MI; (2) left temporal occipital fusiform cortex; (3) right putamen; (4) and right anterior
parahippocampal cortex (all ps<0.05, SVC). No whole-brain seed-to-voxel results were
observed. A full table of results can be found in Table 10 and Figures 12B & S3B.
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Table 10. Seed-to-voxel activations for the Disgust Faces condition for the seeds: right midinsula (MI) and left medial orbitofrontal cortex (mOFC; p<0.05, SVC)
Seed Contrast Max Z Cluster size
(p<0.05) X Y Z Laterality Region
Right MI TD>ASD 2.68 7 -8 24 22 L Anterior cingulate cortex
(ACC)
2.05 10 -16 -8 -20 L Hippocampus
2.21 16 -22 40 -12 L Ventromedial prefrontal cortex
(vmPFC)
3.04 38 34 16 4 R Dorsal anterior insula (dAI)
2.46 22 46 8 -4 R dAI
2.5 15 2 34 -22 R Medial orbitofrontal cortex
(mOFC)
1.94 3 40 -14 4 R Posterior insula (PI)
2.11 16 34 22 -6 R Ventral anterior insula (vAI)
ASD>TD 2.32 7 -20 4 -24 L Amygdala
2.37 9 -20 -4 -32 L Anterior parahippocampal
cortex
2.66 32 -38 4 -14 L Mid-insula (MI)
1.87 3 -10 -16 42 L Mid-cingulate cortex (MCC)
1.8 3 -40 -14 12 L PI
2.48 27 28 4 -20 R Amygdala
2.08 8 28 -8 -14 R Amygdala
2.12 6 44 8 -18 R Temporal pole
Left mOFC TD>ASD 2.88 32 -34 8 4 L dAI
2.09 7 -44 12 -6 L dAI
3.4 15 -24 56 32 L Frontal pole
2.73 3 -20 56 34 L Frontal pole
1.87 4 -22 -6 -28 L Hippocampus
2.06 3 -36 -12 18 L PI
2.19 21 -46 16 -36 L Temporal pole
2.33 8 -30 10 -14 L vAI
2.65 10 2 30 18 R ACC
2.16 33 36 0 0 R dAI
2.48 23 36 20 4 R dAI
2.34 14 40 14 -6 R dAI
2.91 17 4 6 24 R MCC
1.87 3 24 10 -24 R Orbitofrontal cortex (OFC)
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2.15 16 38 -16 12 R PI
2.33 10 4 20 66 R Superior frontal gyrus
2.15 7 36 -44 -20 R Temporal occipital fusiform
2.31 9 36 22 -42 R Temporal pole
2.29 7 38 16 -14 R vAI
2.72 28 2 36 -22 R vmPFC
1.99 3 10 64 -4 R vmPFC
ASD>TD 2.29 10 -38 2 -16 L MI
2.58 14 -38 -50 -12 L Temporal occipital fusiform
2.05 4 24 -2 -32 R Anterior parahippocampal
cortex
1.89 7 32 -8 -10 R Putamen
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Figure 12. Psychophysiological interaction (PPI) results for co-activations (nodes) and
connections (edges) of the right mid-insula (A) and the left medial orbitofrontal cortex (B) in the
Disgust Faces condition. Hypoconnectivity in ASD and TD>ASD co-activations appear in blue,
while hyperconnectivity in ASD and ASD>TD co-activations appear in red. dAI = dorsal
anterior insula; vAI = ventral anterior insula; MI = mid-insula; PI = posterior insula; vmPFC
= ventromedial prefrontal cortex; mOFC = medial orbitofrontal cortex; Amyg = amygdala; Put
= putamen; ACC = anterior cingulate cortex; MCC = middle cingulate cortex.
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3.3.3. Brain-behavior correlations in ASD
a. Disgust Food condition
In the ASD group, when looking at disgusting foods, connectivity between the right MI
and the left dorsal AI (dAI) was significantly positively correlated with both disgust sensitivity
(DS: R = 0.49, p = 0.013; see Figures 13A and 14) and disgust propensity (DP: R = 0.513, p =
0.009; see Figures 13A & 14). Further, in the ASD group, when looking at disgusting foods,
connectivity between the right MI and left dorsal AI was significantly positively correlated with
gustatory sensitivity (R = 0.407, p = 0.043) and AQC 2-factor (R = 0.519, p = 0.008; see Figure
13A). After adjusting for gustatory sensitivity, alexithymia, and IQ in the ASD group, the right
MI - left dAI connectivity remained significantly positively correlated with DS (r = 0.493, p =
0.02), but no longer correlated with DP (r = 0.351, p = 0.11). Other significant brain-behavior
correlations for the right MI seed region included: left ventral AI (vAI) and NEPSY ToM (R = -
0.487, p = 0.014); right ventral AI and NEPSY ToM (R = -0.437, p = 0.029); left amygdala and
ADOS RRB (R = 0.556, p = 0.004); and right amygdala and ADOS RRB (R = 0.56, p = 0.004).
On the other hand, in the ASD group, the connectivity between right mOFC and other
regions was not significantly correlated with DS or DP. Other significant brain-behavior
correlations for the right mOFC seed region included: left dorsal AI and gustatory sensitivity (R
= 0.401, p = 0.047); left pallidum and AQC 2-factor (R = -0.397, p = 0.049); left pallidum and
NEPSY ToM (R = 0.502, p = 0.011); right amygdala and NEPSY ToM (R = -0.48, p = 0.015);
left MI and BPQ (R = -0.468, p = 0.018); and left amygdala and ADOS RRB (R = 0.472, p =
0.017; see Figure 13B).
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Figure 13. A. Correlation plot of Pearson’s correlations between selected right mid-insula (MI)
ROIs during the Disgust Food condition and main behavioral and demographic variables in the
ASD group. B. Correlation plot of Pearson’s correlations between selected right medial
orbitofrontal cortex (mOFC) ROIs during the Disgust Food condition and main behavioral and
demographic variables in the ASD group. Note: DS = Disgust sensitivity; DP = Disgust
propensity; BPQ = Body Perception Questionnaire - Very Short Form; AQC = Alexithymia
Questionnaire for Children; NEPSY = NEuroPSYchological behavioral assessment; ToM =
Theory of mind; ADOS = Autism Diagnostic Observation Schedule 2nd edition; SA = Social
affect; RRB = Restricted and repetitive behaviors.
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Figure 14. Scatter plots with regression lines across groups (black) and for each group (TD:
green circles; ASD: orange triangles) for the Disgust Food condition for disgust sensitivity and
propensity with the connectivity between the right mid-insula and the left dorsal anterior insula
(dAI). Note, correlations are only significant in the ASD group (p<0.05).
b. Disgust Faces condition
In the ASD group, when looking at disgust facial expressions, connectivity between the
right MI and the left vmPFC was significantly positively correlated with disgust sensitivity (DS:
R = 0.468, p = 0.018, see Figure 15A and 16). Additionally, connectivity between right MI and
the right amygdala was significantly negatively correlated with both DS (R = -0.553, p = 0.004)
and DP (R = -0.515, p = 0.009; see Figure 15A and 16). Connectivity with the left vmPFC was
also significantly positively correlated with AQC 2-factor (R = 0.41, p = 0.042) and negatively
correlated NEPSY ToM (R = -0.507, p = 0.01). On adjusting separately for theory of mind and
alexithymia in the ASD group, the right MI - left vmPFC connectivity was no longer found to be
significantly correlated with DS. The right MI - right amygdala connectivity had no other
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behavioral covariates and remained robust after adjusting for age, IQ and sex in the ASD group.
Furthermore, in the ASD group, connectivity between the left mOFC and left vAI was
significantly positively correlated with tactile sensitivity (R = 0.443, p = 0.037; see Figure 15B).
Figure 15. A. Correlation plot of Pearson’s correlations between selected right mid-insula (MI)
ROIs during the Disgust Faces condition and main behavioral and demographic variables in the
ASD group. B. Correlation plot of Pearson’s correlations between selected left medial
orbitofrontal cortex (mOFC) ROIs during the Disgust Faces condition and main behavioral and
demographic variables in the ASD group. Note: DS = Disgust sensitivity; DP = Disgust
propensity; BPQ = Body Perception Questionnaire - Very Short Form; AQC = Alexithymia
Questionnaire for Children; NEPSY = NEuroPSYchological behavioral assessment; ToM =
Theory of mind; ADOS = Autism Diagnostic Observation Schedule 2nd edition; SA = Social
affect; RRB = Restricted and repetitive behaviors.
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Figure 16. A. Scatter plots with regression lines across groups (black) and for each group (TD:
green circles; ASD: orange triangles) for the Disgust Faces condition for disgust sensitivity and
the connectivity between the right mid-insula and left ventromedial prefrontal cortex (vmPFC).
B. Scatter plots with regression lines across groups (black) and for each group (TD: green
circles; ASD: orange triangles) for the Disgust Faces condition for disgust sensitivity and
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propensity with the connectivity between the right mid-insula and the right amygdala. Note,
correlations are only significant in the ASD group (p<0.05).
3.4. Discussion
While many studies have focused on potential emotion processing differences in autism,
whether and how much these differences are due specifically to the emotion of disgust has been
less explored. Nevertheless, prior ASD research indicates differences in disgust processing
(Jayashankar, Kilroy, et al., 2022; Jayashankar et al., under review; Kalyva et al., 2010; Siegal et
al., 2011; Zhao et al., 2016), as well as increased pica behaviors (Fields et al., 2021; Mayes &
Zickgraf, 2019). Further, to our knowledge, while there is strong support that connectivity
differences at least partially underlie ASD, to our knowledge no studies have looked at
functional connectivity in ASD during disgust processing. Here, for the first time, we show that
the ASD group has numerous functional connectivity differences when processing disgust.
Specifically, across conditions, we found in the ASD group: 1) hypoconnectivity between all
seed regions (right mid-insula [MI], right & left medial orbitofrontal cortex [mOFC]) and the
bilateral dorsal anterior insula (dAI)); 2) hypoconnectivity between the right MI and left
ventromedial prefrontal cortex (vmPFC) ; 3) hyperconnectivity between the right MI and
bilateral amygdala; 4) hyperconnectivity between the right and left mOFC and the left MI; and 5)
hyperconnectivity between the right and left mOFC and reward-processing regions (putamen,
pallidum). Additionally, for both seeds (right MI, left mOFC), when viewing disgust facial
expressions, we see hypoconnectivity with emotion-related regions (dAI, ventral AI, vmPFC).
Interestingly, of all these regions, in the ASD group, the connectivity between the right
MI and left dAI during the observation of disgusting foods and the connectivity between the
right MI and right amygdala during the observation of disgust facial expressions are significantly
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associated with individual differences in disgust proneness, above and beyond the effects of
other behavioral measures (sensory behavior, alexithymia). Additionally, the common origin in
the right MI for both these disgust-related connections further supports our previous hypothesis
that the right MI may serve as a hub for processing unpleasantness or repulsion (Jayashankar et
al., under review). Below, we discuss these findings in detail and their implications for better
understanding disgust processing in autism.
3.4.1. Hypoconnectivity in emotion-related regions in ASD
In autism, previous research has shown that the insula commonly shows
hypoconnectivity, particularly in social tasks (Di Martino et al., 2009; Nomi et al., 2019). In this
study, in the ASD group, the right dorsal AI exhibited significant hypoconnectivity with all
seeds, during all task conditions. This finding is consistent with our general hypothesis that ASD
is associated with reduced connectivity of core disgust processing regions. In particular, the right
insula is implicated in the interoceptive processing of affective information from disgust
experiences (Craig, 2003, 2009; Menon & Uddin, 2010; Safar et al., 2021), and the dorsal
portion is further involved in preparing appropriate responses to affective stimuli (Deen et al.,
2011). While findings regarding the ventral AI are inconsistent, the consistency of dorsal AI
hypoconnectivity may suggest that autistic youth may experience difficulties, perhaps with
interoceptive processes related to disgust processing. We suggest that hypoconnectivity between
the dorsal AI and the MI may further underlie downstream differences in communication with
attentional and executive functioning regions, which may in turn influence inappropriate disgust
responses (Deen et al., 2011). Taken together, reduced coactivation in the right dAI with other
disgust-related seed regions may indicate dysfunctional connectivity with the right dAI,
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highlighting one mechanism contributing to the reported difficulties in disgust processing in
extant literature (Dimopoulou et al., 2006; Kalyva et al., 2010; Yeung, 2022; Yeung et al., 2020).
Also, our findings indicated hypoconnectivity between the left vmPFC and right MI for
both physical and social disgust processing. The vmPFC is a core socio-emotional processing
region and has been implicated in emotion regulation (Pitskel et al., 2014), mentalizing about
emotional stimuli (Mendez, 2023), and directing visual attentional focus to emotional facial
expressions (Wolf et al., 2014). The right MI is implicated in chemosensation (for a review, see
Uddin et al., 2017), particularly in the processing of the affective value of olfactory and gustatory
stimuli (Pritchard et al., 1999; Stevenson et al., 2015). Thus, hypoconnectivity in ASD between
the MI and vmPFC could represent a dysfunction in the neural circuit involved in
communicating affective value of disgust-related stimuli for further socio-emotional and emotion
regulation processes (Chen et al., 2021; South & Rodgers, 2017). Such hypoconnectivity is in
line with our general hypothesis, and may help explain differences in disgust processing
commonly seen in autistic youth (Dimopoulou et al., 2006; Jayashankar et al., under review;
Kalyva et al., 2010; Yeung, 2022; Yeung et al., 2020).
3.4.2. Hyperconnectivity in emotion-related regions in ASD
The amygdala is an important part of the limbic system, involved in processing disgust
feelings, the salience network, and emotional face processing systems (Fusar-Poli et al., 2009).
Activity within and between the amygdala and the insula plays an important role during disgust
processing (Gan et al., 2022). Additionally, amygdala integrity is necessary for adequate emotion
regulation and behavioral responses. In our previous work, we identified reduced activity in the
amygdala in the ASD group during the processing of both physical and social disgust
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(Jayashankar et al., under review). Here, we see in ASD, that previously observed hypoactivity
within the amygdala (see Chapter 2) may be compensated by hyperconnectivity between the
amygdala and right MI, potentially providing an alternate circuit for processing disgust
information. Behaviorally, these activity and connectivity differences may relate to behavioral
differences seen in ASD when processing emotional facial expressions. Indeed, prior studies
indicate that autistic youth may experience attentional biases when viewing emotional facial
expressions (initially hypervigilance of disgust facial expressions, then later avoidance of such
faces; Zhao et al., 2016). Additionally, the amygdala has been shown to be more active and
connected in the ASD during implicit threat processing of emotional facial expressions (Chen et
al., 2021). In fact, implicit (and not explicit) threat processing of disgusting stimuli predicts
behavioral responses and avoidance of contaminating stimuli (J. S. Green & Teachman, 2013).
Thus, our findings of significant amygdala hyperconnectivity in ASD during the observation of
disgust facial expressions may reflect a combination of attentional bias to facial expressions and
the implicit threat evaluation of these facial expressions. Taken together, the connectivity
differences between the right MI and amygdala and other emotion processing regions (dAI,
vmPFC) may underlie inappropriate behavioral responses to physical and social disgust elicitors.
Finally, we discuss hyperactivity between the OFC and putamen in the ASD group. The
putamen has been linked with disgust processing previously (Calder et al., 2007; Hennenlotter et
al., 2004; Sambataro et al., 2006). Further, prior work indicates in ASD, hypoactivity of the
putamen during emotion and reward processing tasks (Janouschek et al., 2021; Nickl-Jockschat
et al., 2012). Here, similar to the finding of the amygdala, compensatory hyperconnectivity may
be related to disgust-related emotion processing difficulties in autism, though further work is
needed to better understand these patterns.
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3.4.3. Connectivity associated with disgust proneness (trait disgust)
When looking at individual differences in disgust propensity, results indicate that when
looking at disgusting foods, connectivity differences in the ASD group between the left dAI and
right MI varied with disgust sensitivity and propensity. Specifically, hypoconnectivity between
the right MI and left dAI when observing disgusting foods is associated with reduced trait levels
of disgust sensitivity and propensity. This aberrant connectivity pattern being linked with disgust
propensity may help explain some pica and food-related behaviors common to autism (Fields et
al., 2021; Mayes & Zickgraf, 2019) and inappropriate response behavior to such contaminants
(Kalyva et al., 2010; Siegal et al., 2011). Additionally, the connectivity between the left dAI and
right MI increased with higher trait levels of disgust sensitivity and propensity. Such increased
connectivity at higher disgust trait levels may indicate that in some autistic youth, there is an
over-responsiveness of the dorsal AI to disgusting stimuli in autism. This could possibly underlie
another form of disgust processing difference in autism, perhaps influenced by sensory
processing differences (we previously found positive correlation with tactile sensitivity; see
Figure 4A; Jayashankar et al., under review) and resulting in food pickiness. However, such
inference was beyond the scope of this study, as our ASD sample did not have many with higher
disgust proneness.
Additionally, we observed that during social disgust processing in the ASD group, higher
levels of disgust sensitivity and propensity traits were significantly negatively associated with
connectivity between the right MI and right amygdala. Specifically, higher right MI-amygdala
connectivity is observed in autistic participants with lower disgust proneness traits. It may be that
amygdala hyperconnectivity serves as a compensatory mechanism for amygdala hypoactivity
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found when viewing disgust facial expressions (Jayashankar et al., under review). Furthermore,
the disgust-associated amygdala hyperconnectivity may suggest that, despite the reduced activity
in social disgust regions, autistic youth may nevertheless process information conveyed by
disgust facial expressions (Chen et al., 2021; J. S. Green & Teachman, 2013). Future research on
larger ASD samples may help shed more light on this mechanism and how it affects social
disgust behavior in autism.
3.4.4. Lateralization of functional connectivity differences
A recurring theme in our results is how the laterality of certain findings leads to diverging
trends in connectivity. For instance, during the Disgust Food condition, we observed reduced
connectivity in the right dAI, but increased connectivity in the left dAI. Additionally, our
previous data (Jayashankar et al., under review) found reduced activity in the right MI, but found
hyperactivity within the left MI in both conditions. Indeed, previous research on the functional
connectivity in autistic youth during social processing is extensive (although not for disgust
particularly) and sheds some light on our findings (Hull et al., 2017; Ilioska et al., 2023; J. M.
Lee et al., 2016; Sigar et al., 2023). Previous research also has shown that the right insula
develops earlier, but stops developing earlier too, as compared to the left insula (Carpenter,
1991). Reported findings of reduced connectivity in ASD have also been shown to be influenced
primarily by intra-hemispheric losses in connectivity (J. M. Lee et al., 2016). Additionally, prior
connectivity research found distinct patterns of connectivity associated with the right and left
insula, with the right insula being more important for attention and executive functioning
required for preparing appropriate responses to disgusting stimuli (Cauda et al., 2011; Deen et
al., 2011). While this helps explain our findings of reduced activity in the right insula, it does not
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shed light on the hyperactivity of the left insula. We believe such connectivity differences could
imply that while there may be the disintegrity of the right insula in autism, the left MI may have
taken on the role of processing the disgust information previously specialized by the right MI in
non-autistics. However, such inference is beyond the scope of this study and further research is
needed.
Another distinct pattern of connectivity presented in ASD during the Disgust Food
condition was the selective hyperconnectivity in the contralateral OFC associated with the
activity of both right-localized seeds, implying a negative correlation between the left OFC
activity and both MI and OFC on the right side. This pattern of left OFC hyperconnectivity was
also observed in the Disgust Faces condition in previous research (Jayashankar et al., under
review). As the OFC is involved in the evaluation of the emotional valence of stimuli (Yang et
al., 2021), this finding could imply a compensatory mechanism in the ASD group. In fact, this
study has found that the hyperactivity in the left mOFC is significantly associated with reduced
activity in the right mOFC during the Disgust Faces condition, implying a similar compensatory
mechanism for evaluating the emotional valence of disgusted faces. Additionally, the functional
integrity of the amygdala-to-OFC connection is important for emotion identification (Yang et al.,
2021). We observed such hyperactivity in both the left amygdala and OFC, but no difference in
the ipsilateral right amygdala activity, associated with the right mOFC during the Disgust Food
condition. This further supports the notion of a compensatory valence evaluation system by
which autistic youth may adequately evaluate disgusting foods. Further investigation is required
into the impact of dysfunctional connectivity between the left amygdala and left OFC during
physical disgust processing.
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3.4.5. Limitations
One caveat of this study is the lack of comparison in functional connectivity of disgust
against other basic emotions (particularly fear and sadness), which would help to determine
whether these differences are influenced by disgust or general emotion processing. However,
while this study focused only on functional connectivity during disgust, previous research in
non-autistic individuals has shown that the basic emotions differ consistently and sufficiently
enough in their functional connectivity to be classified as distinct emotion-specific network
activity states (Saarimäki et al., 2022). Since we identify functional connectivity differences
within regions associated with disgust-specific processing, we expect these results to sufficiently
reflect disgust-specific changes in functional connectivity between ASD and TD participants.
Additionally, autism is a highly heterogeneous condition (Masi et al., 2017) and we
restricted this study to only high-functioning right-handed autistic youth in order to lower
variance in our brain imaging data. Thus, our conclusions do not offer insights into the disgust
processing connectivity differences in autistic populations with intellectual disabilities. Our ASD
sample was also biased towards males, due to difficulties in recruiting autistic females. Future
studies could expand on our findings by utilizing larger and more heterogeneous sample sizes
(e.g., n>2000; see Marek et al., 2022).
3.5. Conclusion
The present study employed a functional connectivity analytic approach to determine the
connectivity differences during physical and social disgust processing in autistic and non-autistic
youth. Across conditions, the right dorsal AI revealed significantly lower connectivity in the
autistic group. The broader context highlighted by the findings is that there are significant neural
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connectivity differences in autistic individuals when exposed to disgusting foods and facial
expressions of disgust. Specifically, the left dorsal anterior insula and the right amygdala show
notable patterns of hypo- and hyper-connectivity during these observations. Importantly, these
connectivity differences interact with individual variations in disgust proneness. Understanding
these neural connectivity variations provides crucial insights into the neurobiological
underpinnings of distinctive experiences in autistic youth, particularly in food behaviors,
contamination, and social interactions. This knowledge contributes to a more comprehensive
understanding of how autistic youth process and respond to stimuli associated with disgust.
Future research will help expand on these findings and shed light on whether and how these
differences may be indirectly linked with other conditions, like pica or obesity. Such insights can
have implications for designing targeted interventions and support strategies tailored to the
specific needs of individuals on the autism spectrum in these domains. In essence, the bigger
picture involves unraveling the neural basis of sensory and social differences in autism, paving
the way for more informed and personalized approaches to support and care.
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CHAPTER 4: THE INFLUENCE OF DISGUSTING ODORS ON MORAL DISGUST IN
AUTISTIC YOUTH
Abstract
This study delves into the intricate relationship between disgust and moral decisionmaking in typically developing (TD, n=17) children and with autism spectrum disorder (ASD,
n=13). All youth (aged 8-17) completed a survey where they were asked to judge felt wrongness,
deserved punishment and permissibility/allowability of actions in moral/purity violations,
physically disgusting non-moral situations, and neutral negative affect situations, presented
through vignettes. The survey was completed either in a room with a disgusting odor or in a noodor room. Based on prior data indicating that autistic youth had elevated disgust proneness
(disgust sensitivity and disgust propensity), we predicted that autistic youth in the disgusting
smelling room who have increased disgust proneness would make harsher judgments. The
groups were compared on ratings of perceived wrongness, necessary punishment, and
permissibility of violations. Results indicate that autistic individuals with higher disgust
propensity gave harsher wrongness and permissibility ratings of purity violations when in the
disgusting smelling room as compared to the TD group. Notably, the study demonstrates that
participants experienced strong feelings of wrongness related to moral/purity violations, and that
these and assignment of harsher punishments were significantly stronger in the ASD group. The
influence of disgust priming on purity violation moral ratings in ASD underscores the link
between purity violations, physical disgust and the moral disgust in autism; a relationship that
has previously not been studied. In essence, our research provides an understanding of how
sensory sensory and disgust proneness are related to embodied feelings of moral disgust in
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autism. These results may have implications for better understanding of autism in everyday
social situations, offering valuable insights into the potential influence of naturally-occuring
physically disgusting stimuli on moral feelings of wrongness and allowability of autistic youth.
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4.1. Introduction
Contrary to literature suggesting that autistic youth display lower disgust proneness
(Kalyva et al., 2010), our previous data has shown that autistic youth have greater disgust traits:
disgust sensitivity and disgust propensity (Jayashankar et al., under review). Disgust propensity
is the frequency of experiencing disgust and disgust sensitivity is the degree of negative appraisal
of disgust experiences, and together they are collectively known as disgust proneness (Olatunji &
Cisler, 2009). In non-autistic individuals, excessive disgust proneness can influence moraldecision making, especially moral violations pertaining to impurity (Inbar & Pizarro, 2022;
Schnall, Haidt, et al., 2008). Hence, in autistic individuals, this finding of elevated disgust
proneness in autism (ASD) potentially has implications for moral decision-making, especially
given previous reports that ASD individuals may display harsher moral judgments (Bellesi et al.,
2018; Margoni et al., 2019; Margoni & Surian, 2016).
Other factors can also influence moral judgments in both non-autistic and autistic
individuals. These factors include interoception ability and alexithymia (Mul et al., 2018;
Oakley et al., 2016), both of which may be especially impacted in autism. In autism, there are
potential variations in individual interoception ability (Butera et al., 2023; Butera et al., 2022;
DuBois et al., 2016; Mul et al., 2018), and an elevated prevalence of co-occurring alexithymia
(55%; refer to Milosavljevic et al., 2016; Vaiouli et al., 2022). Despite this, to the best of our
knowledge, there is a paucity of studies on this subject. Subsequent research efforts are
warranted to gain a deeper understanding of the potential implications of differences in disgust
processing, in conjunction with sensory processing, alexithymia, and interoception processing
variations, on moral judgment disparities in autism. Here, we first summarize the extant literature
on moral decision-making in autism and how core/physical disgust can bias moral judgments.
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We then introduce our study, which explores the differences in moral decision-making between
autistic and non-autistic youth and the relationship within autism between physical disgust,
moral decisions, and other behaviors.
4.1.1. Moral decision-making in autism
Researchers have highlighted the importance of understanding the intentions of moral
actors (theory of mind [ToM]/mentalizing), recognition of the victim’s emotional state, roletaking, and empathy (Baron-Cohen et al., 1985; Bos & Stokes, 2019; Dziobek et al., 2008;
Mazza et al., 2014; Patil et al., 2016; Rueda et al., 2015). In autism, prior work indicated that
autistic individuals may exhibit delayed mentalizing abilities in comparison to their non-autistic
counterparts (Andreou & Skrimpa, 2020; Fadda et al., 2016; Jayashankar et al., 2023; Zemestani
et al., 2022), but with additional time, young autistic individuals demonstrate the capacity to
attribute mental states to others (Baron-Cohen et al., 1985; Senju et al., 2009). However, a
comprehensive review highlighted that when autistic individuals assess situations of accidental
harm (e.g., accidentally tripping someone because your foot is out), they tend to engage in more
outcome-based judgments than intention-based judgments as compared to their non-autistic peers
(Margoni & Surian, 2016). However, this finding is disputed, as a newer study proposed that
autistic children exhibit similar judgments to their non-autistic peers (Dempsey et al., 2020), thus
further work is warranted.
Nevertheless, autistic youth may display differences in complex moral reasoning during a
rule transgression task, such as the elaborating on the rationale for someone lying at a job
interview (Bellesi et al., 2018) as well as harsher judgments for both intentional and
unintentional actions within a social intentionality task (e.g., confidant divulging embarrassing
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information, whether purposefully or inadvertently (Bellesi et al., 2018). However, a recent study
indicated that judgments across groups may be more influenced by the perceived wrongness of
transgressions rather than group membership (autism or neurotypical; Dempsey et al., 2022). For
example, when presented “You see a boy/girl loudly burping and farting while eating. Is this
bad?”, how bad autistic and non-autistic participants perceived this action determined their
responses irrespective of clinical group. Still, autistic children were found to advocate for
significantly harsher punishment for moral transgressions compared to the comparison group,
particularly in cases involving social norm violations (unusual, strange acts that are not
associated with other moral foundations; Dempsey et al., 2022). Contrary to this finding,
Margoni and colleagues (2019) argue that autistic children are proficient in making intent-based
moral judgments, but differences between groups may arise from differences in executive
function ability. They note that explicit descriptions of moral agent intentions are crucial for
accurate assessments in autistic individuals, while unclear descriptions lead to outcome-based
moral decisions and harsher punishment of unintentional moral agents (Bellesi et al., 2018; Buon
et al., 2013; Grant et al., 2005; Koster-Hale et al., 2012; Salvano-Pardieu et al., 2016). This is
consistent with prior reports of difficulties with intention understanding in autism (Koster-Hale
et al., 2012; Margoni & Surian, 2016; Young et al., 2010). Intriguingly, a study using the trolley
problem (a classic moral dilemma; Jarvis Thomson, 1985) reveals opposing effects of autistic
and alexithymic traits on utilitarian decision-making (outcome-based decisions). Higher
alexithymic traits, rather than autistic traits, were associated with a greater inclination towards
utilitarianism/outcome-based thinking (Patil et al., 2016). This indicates that alexithymia (which
is common to autism, Cuve et al., 2022; Milosavljevic et al., 2016), rather than autism per se,
may be associated with differences in moral judgments.
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4.1.2. The effect of physical disgust on morality in non-autistics
Previous data in typically developing (TD) individuals indicates that embodied feelings
of disgust may impact moral judgments. For example, exposure to physically repugnant stimuli
can influence moral judgment of others’ actions (Inbar & Pizarro, 2022; Schnall, Haidt, et al.,
2008; Tracy et al., 2019; Wicker et al., 2003). This phenomenon may, in part, arise from the
shared behavioral and facial responses induced by both physical (associated with risks of
physical contamination) and socio-moral disgust (associated with vicarious processing and risks
of moral taint), as evidenced in studies on oral-nasal rejection, the physical rejection response to
disgusting smells or tastes (Chapman et al., 2009; Chapman & Anderson, 2013; Ekman et al.,
2013).
The intricate link between disgust and moral judgment is further delineated by Cannon
and colleagues (2011), who associated disgust facial responses directed towards moral
transgressions concerning purity and fairness, while expressions of anger are more aligned with
harm violations. The positive correlation between the intensity of disgust facial responses
directed at transgressions and the perceived severity of moral violations is consistent and evident
even in children (Danovitch & Bloom, 2009). Thus, these studies highlight the close relationship
between disgust and moral contempt (Power & Dalgleish, 2015; Rozin, Lowery, Imada, et al.,
1999). In fact, among the various moral foundations (Clifford et al., 2015), transgressions/acts
that violate purity or sanctity, which encompass degrading behaviors (e.g., drunken groping),
sexually deviant acts, or actions posing a risk of physical or social contamination (e.g., urinating
in a public pool), are particularly strongly associated with the emotion of disgust (Dempsey et
al., 2022; Horberg et al., 2009, 2011). Corroborating this assertion, purity violations strongly
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elicit disgusted facial expressions and reactions (Cannon et al., 2011; Ekman et al., 2013),
indicating a close relationship with disgust feelings. Thus, we expect survey responses to moral
violations, particularly purity violations, to be correlated with disgust proneness traits.
Given this intricate link between disgust and moral evaluation, we may expect that
differences in physical disgust reactivity would impact the judgments of immoral acts. Such
manipulation of moral disgust experiences through olfactory stimuli, either inhibiting or eliciting
nausea, has been explored in non-autistics (Royet et al., 2001; Schnall, Benton, et al., 2008;
Schnall, Haidt, et al., 2008; Tracy et al., 2019). Studies by Wheatley & Haidt (2005) and Schnall
& colleagues (2008) demonstrate that exposure to extrinsic disgusting odors leads to heightened
moral evaluations. The impact is observed not only through odors but also in environments,
videos, and vivid recollections of disgusting experiences, with the effects more pronounced in
individuals with higher interoceptive awareness (Schnall, Haidt, et al., 2008). We utilize such
paradigms in our current study in ASD.
4.1.3. Present study
Here, we investigated whether physical disgust processing impacts moral violations in
autistic participants (similar to studies in TD groups, described above). We anticipate that such
effects in ASD will be further modulated by differences in disgust processing in autism.
In addition, we plan to account for the intrinsic differences in socio-emotional processing
observed in autistic youth. In autism, there are potential variations in individual interoception
ability (Butera et al., 2023; Butera et al., 2022; DuBois et al., 2016; Mul et al., 2018), and the
elevated prevalence of co-occurring alexithymia (55%; refer to Cuve et al., 2022; Milosavljevic
et al., 2016; Vaiouli et al., 2022). These factors also influence moral decisions in non-autistic
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populations, thus one can anticipate a dynamic interplay of these factors within the autism
spectrum (Mul et al., 2018; Oakley et al., 2016). Thus we plan to investigate in autism, a
multifaceted relationship between physical and moral disgust processing, shaped by disgust
processing, interoceptive ability, and the presence of alexithymia.
Participants were presented with a sample of vignettes that include morally
violating/disgusting situations (e.g., urinating in a public pool), physically disgusting non-moral
situations (e.g. touching a worm), and neutral negative affect situations (e.g. watching a sad
movie). Following each vignette, they were asked to rate: 1) their perceived feelings of
wrongness of the actions taken by the main character in the story; 2) degree of necessary and
deserved punishment for the violation; and 3) permissibility/allowability of the violation. To
assess the effects of odor priming on moral decisions, half of the participants (randomly
assigned, double-blind) completed the survey in a room with a disgusting odor while the other
half completed the survey in an odorless room.
Our study had three primary hypotheses. Given the mixed literature on general moral
decision-making and disgust processing in autism as compared to non-autistics, we compared
our ASD and TD groups in initial analysis of survey ratings to determine the pattern of moral
evaluation and disgust proneness for our current ASD sample. This would help us to identify
with which side of the literature our findings would align and determine the anticipated
interaction effect of physical disgust processing and autism symptomatology on moral disgust
decisions. Firstly, consistent with some of the literature, we predict that across conditions, the
ASD group will have harsher moral judgments than the TD group. In addition, in line with our
previous work that found sensory sensitivities and increased disgust proneness in ASD
(Jayashankar et al., under review), we expected that our ASD participants would present with
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higher disgust proneness than the TD group. Secondly, we predicted that such disgust differences
and placement in the room with the disgusting smell will interact and impact the moral
evaluations of the ASD group (harsher moral purity judgments in the room with the disgusting
smell), due to increased sensory sensitivity and disgust proneness. Thirdly, we predicted that
increased alexithymia and interoception differences (as measured by a heart-beat counting task)
will correlate with moral decisions, but the effects of physical disgust on moral decision-making
will be more robust in the ASD group. Finally, as a subordinate hypothesis, we predicted that all
participants would rate moral violations closely associated with disgust (purity violations) more
harshly than any physically disgusting non-moral and neutral negative affect vignettes.
4.2. Materials and Methods
4.2.1. Participants
We primarily recruited ASD participants from our prior studies, and used advertisements
on social media to recruit additional ASD and TD participants. All participants were between
ages 8-17 years. From here on, we use ‘youth’ to refer to our participant samples.
High-Functioning Youth with ASD (N=17; 9 with no odor; 8 with odor)
Eligibility criteria included: (a) prior clinical diagnosis of ASD using the Autism
Diagnostic Observation Schedule-2 (C. Lord et al., 2012) and confirmed using the Autism
Spectrum Quotient (AQ; Allison et al., 2012; Auyeung et al., 2008; Baron-Cohen et al., 2006);
(b) IQ≥80 on the Wechsler Abbreviated Scale of Intelligence, 2nd Edition (WASI-II, Wechsler,
2011); (c) no prior or concurrent diagnosis of other major neurological or psychiatric disorders
mentioned during pre-screening of participants; and (d) English speaking children and parents, as
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our standardized measures were in English only. An IQ cut-off of ≥80 was used to ensure that
participants can understand task directions.
Typically Developing (TD) Non-autistic Youth (N=13; 6 with no odor; 7 with odor)
We made our best efforts to match the TD group for age and gender. Other inclusion
criteria were: (a) no ADHD as identified by the parent through screening with the Conners 3
(Conners et al., 2011); (b) no first degree relatives with ASD and no current or previous concerns
about an ASD diagnosis; and the (b)-(d) criteria pertaining to the ASD group.
4.2.2. Task and Environment
4.2.2a. Moral decision-making task
This task was developed by our lab using concepts from and a modified version of the
Moral Foundations Theory questionnaire (Clifford et al., 2015). A pilot study was conducted in
11 participants to shortlist 15 out of the 63 MFT questions to be used in our survey. A detailed
description of this pilot study can be found in Appendix ii.
The participants were asked to rate perceived wrongness, deserved punishment, and
permissibility/allowability of the immoral act of different actors in 45 vignettes, fifteen per
category of situation (moral violation, physical disgust, and neutral negative). Participants had a
choice of reading the vignettes or having them read out in the attached pre-recorded audio files.
The average reading level for all vignettes was at a fifth grade level. Participants rated the actors
on a 7-point Likert scale for wrongness and permissibility and a 5-point Likert scale for
punishment (Chapman & Anderson, 2014). Punishment and permissibility ratings were only
recorded for the moral violations. For wrongness, the Likert scale was defined with ‘0’ being not
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wrong at all and ‘6’ being extremely wrong. For punishment, the scale was defined with ‘0’
being no punishment and ‘4’ being extreme punishment. For permissibility, the scale was
defined with ‘0’ being completely allowed and ‘6’ being absolutely not allowed. A sample
question is provided in Table 11.
4.2.2b. Odor vs. No-odor Environment
Half of the participants were placed in a room with a disgusting odor (Liquid Fart Joke
spray, Forum Novelties). Participants were randomly assigned into either the odor or non-odor
condition and counterbalanced at the beginning of data collection. This was a double-blind study
and neither the participant nor the experimenter had any knowledge of their room assignment
before the end of data collection. The disgusting odor sprayed three times onto a paper towel
covered in petroleum jelly, kept hidden within six feet away from the participant. The other half
of the participants completed the study in an odorless room.
The protocol for this experiment (Jayashankar, Aziz-Zadeh, et al., 2022) can be found
and is publicly available on the Open Science Framework (OSF).
Table 11. Sample vignette (purity violation) and questions for perceived wrongness, necessary
punishment and permissibility (Modified from Dempsey et al., 2022 and Schnall, Haidt, et al.,
2008)
Vignette: "You see a man without a face mask sneeze in another woman's face while walking
down the street."
Types of questions
Wrongness question Do you feel that this is morally wrong or not wrong on a scale of 0 - 6?
Punishment question How much punishment does this person deserve? (0 - 4 scale)
Permissibility question Do you feel that this person should be allowed to do this? (0 - 6 scale)
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4.2.3. Behavioral measures
Individual Differences in Disgust Processing. Variability in disgust processing at the trait
level was evaluated using the Disgust Propensity and Sensitivity Scale – Revised Child version
(DPSS-R; Georgiadis et al., 2020; Olatunji et al., 2007). This self-report questionnaire measures
two components: (a) the frequency of disgust experiences, termed disgust propensity (DP); and
(b) the emotional impact of disgust stimuli, referred to as disgust sensitivity (DS). Comprising 15
items scored on a five-point Likert scale (Cavanagh & Davey, 2000; Olatunji et al., 2007), the
DPSS-R demonstrated strong test-retest reliability and high internal consistency (DP ICC = 0.69,
DS ICC = 0.77; van Overveld et al., 2006). It also exhibited moderate convergent validity with
the Disgust Scale (Haidt et al., 1994) and Disgust and Contamination Sensitivity Questionnaire
(Rozin et al., 1984).
Sensory Sensitivities. Sensory sensitivities were assessed using the Sensory Experiences
Questionnaire Version 3.0 (SEQ; Ausderau et al., 2014; Baranek, 2009; Baranek et al., 2006), a
parent-reported measure comprising 105 items scored on a five-point Likert scale. The SEQ
evaluates four sensory response patterns, five modality-specific sensory sensitivities, and two
sensory experience contexts. Factor analysis confirmed the reasonable fit of the SEQ structure,
with strong and significant factor loadings.
Additional Measures: Interoception, Alexithymia. Interoceptive awareness and
alexithymic traits were gauged through the Body Perception Questionnaire Very Short Form
(BPQ-VSF; Cabrera et al., 2018; Porges, 1993) and the Alexithymia Questionnaire for Children
(AQC; Rieffe et al., 2006) respectively. The BPQ-VSF, a 12-item child report, demonstrated
high test–retest reliability (ICC= 0.97) and good internal consistency (ω = 0.91; Cabrera et al.,
2018). The AQC, with three subscales (difficulty identifying feelings, difficulty describing
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feelings, and externally-oriented thinking), showed internal consistencies (ɑ) of 0.73, 0.75, and
0.29 respectively. The AQC's externally-oriented thinking subscale was excluded due to low
consistency, and a sum total score of the remaining two subscales (AQC 2-factor) was computed
(C. D. Butera et al., 2023; Loas et al., 2017).
Measuring Interoceptive Ability: Heartbeat counting task. The heartbeat counting task
(Dale & Anderson, 1978; Hart et al., 2013; Schandry, 1981) was developed as an empirical test
of interoception, specifically cardioception (Brener & Ring, 2016). This task involves asking the
participants to count their heart beats, without taking their pulse, for a short period of time (25s,
20s and 15s intervals; 1 min. total) and being told to report the counted number of beats. The
onset and offset of counting is signaled with a stopwatch. We used one baseline trial in which the
participant reported their pulse (finger on neck) after 60 seconds, followed by 90 seconds of
exercise (jumping jacks), then three trials in which the participant reported the number of
heartbeats after 25, 45 and 60 seconds respectively. For the post-exercise trials, participants were
instructed to close their eyes and focus on their heart pumping and attempt to count their
heartbeats without taking their pulse. We estimated the fraction by how much the participant
either underestimates or overestimates their heart rate by calculating the difference between the
participant’s subjective count and an objective measure of heartbeats measured using an E4
Empatica photoplethysmograph. For each post-exercise trial, we calculated the absolute
difference between subjective and objective readings. Then, we averaged the absolute difference
across the three trials to determine a single measure for interoceptive awareness. Higher mean
mismatch scores indicated less interoceptive awareness.
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4.2.4. Statistical analysis
Initial analysis assessed the normality and linearity of the distribution of responses. Rank
transformations will be applied if normality or linearity is violated. After verifying if each
subject answered each of the survey questions, median imputation was used for missing data.
Only extreme outliers were excluded from the analysis. Given the normality of distributions in
behavioral measures, for all participants, we compared measures between the groups using
Student’s t-tests to identify group differences. Spearman’s correlations were used to assess the
association of survey ratings with behavioral measures, particularly disgust processing, sensory
processing, alexithymia and interoception. For the purposes of this study, we calculated a
separate score for wrongness, punishment and permissibility for only the purity violations. For
the non-odor and odor conditions, survey ratings were compared using mixed-effects analysis of
variance (ANOVA), treating wrongness, punishment and permissibility as dependent variables in
separate models for each. To assess the influence of behavior on response bias, a three-way
mixed-effects analysis of covariance (ANCOVA) was performed to compare survey ratings
using one within-group/repeated measures factor (moral violations, purity violations, physically
disgusting, neutral negative) and two between-group factors: two groups (TD and ASD) and two
conditions (non-odor and odor-primed subgroups), after adjusting for potentially significant
demographic (age, IQ, gender) and behavioral covariates (disgust proneness, sensory sensitivity,
interoception, alexithymia). While we used parametric mixed-model ANOVAs and ANCOVAs
to compare the survey ratings (wrongness, punishment, permissibility), we performed twosample Mann-Whitney U-tests for multiple comparisons corrections testing within each group
due to small sample sizes. The trend of change in moral ratings after odor-priming in both groups
was assessed statistically. To adjust for the issue of multicollinearity, only one of the collinear
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variables was chosen as a proxy. For instance, the gustatory SEQ subscale was used in
ANCOVAs as a proxy for sensory hypersensitivity.
4.3. Results
4.3.1. Behavioral comparisons and correlations in both groups combined
As Table 12 shows, significant differences were observed between the TD and ASD
groups for the following variables: IQ (TD>ASD), DP (ASD>TD), all SEQ scores (ASD>TD),
AQ (ASD>TD), and AQC 2-factor (ASD>TD). DP was significantly higher in the ASD group as
compared to the TD group, while DS did not significantly differ between groups (p=0.096). DP
was significantly correlated with all sensory scores, primarily gustatory sensitivity (R=0.489,
p=0.006); AQ (R=0.46, p=0.011); AQC 2-factor (R=0.395, p=0.031). Correlations of behavioral
variables across groups (in both groups combined) can be found in Figure 17A. One ASD and
one TD participant were excluded from analysis on wrongness ratings as they were outliers (3
SDs from the mean). One TD participant was excluded from analysis on permissibility ratings as
they were an outlier.
For the wrongness questions (see Figure 17B), DS and DP were significantly and
positively correlated with participant ratings for the moral violations across groups (DS:
R=0.427, p=0.019; DP: R=0.501, p=0.005) and, specifically purity violations (DS: R=0.502,
p=0.005; DP: R=0.585, p=0.001). Additionally, moral violation ratings were also significantly
and positively correlated across groups with gustatory sensitivity (R=0.385, p=0.036), sensory
hypersensitivity (R=0.481, p=0.007), and AQC 2-factor (R=0.368, p=0.045). Also, purity
violation ratings were also significantly and positively correlated with gustatory sensitivity
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(R=0.363, p=0.049), sensory hypersensitivity (R=0.433, p=0.017), and AQ (R=0.387, p=0.034)
across groups.
For the punishment questions (see Figure 17C), DS and DP were significantly and
positively correlated with participant ratings for moral violations (DS: R=0.456, p=0.011; DP:
R=0.5, p=0.005) across groups. Additionally, moral violation ratings were also significantly and
positively correlated with all SEQ scores, primarily gustatory sensitivity (R=0.395, p=0.031);
AQ (R=0.498, p=0.005), and AQC 2-factor (R=0.475, p=0.008) across groups. Also, while
punishment ratings for purity violation were not significantly correlated with DS or DP, they
were significantly and positively correlated with sensory hypersensitivity (R=0.403, p=0.027),
AQ (R=0.507, p=0.004) and AQC 2-factor (R=0.408, p=0.025) across groups.
For the permissibility questions (see Figure 17D), DS and DP were not significantly
correlated with participant ratings for moral or purity violations. However, purity violation
ratings were significantly and positively correlated with AQ (R=0.455, p=0.011) and mean
absolute difference in heartbeat counts/interoception (R=0.415, p=0.023) across groups.
Table 12. Descriptive summary of demographic and behavioral variables and group
comparisons
Group Sig.
TD ASD
(n = 17) (n = 13) t-statistic p-value
Age 12.8 (2.4) 12.9 (2.5) -0.107 0.916
IQ 122.0 (9.4) 111.0 (15.6) 2.4 0.023*
Sex 3.723† 0.054†
Male 9 12
Female 8 1
Condition 0.136† 0.713†
Non-odor 9 6
Odor 8 7
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AQ 2.7 (2.3) 6.6 (2.0) -4.876 <0.001*
Disgust sensitivity (DS) 10.9 (4.1) 13.8 (5.2) -1.723 0.096
Disgust propensity (DP) 19.8 (5.2) 24.8 (7.8) -2.081 0.047*
SEQ Gustatory 20.6 (4.6) 35.8 (12.2) -4.267 <0.001*
SEQ Hyper 38.6 (8.1) 77.3 (22.6) -5.894 <0.001*
SEQ Hypo 19.9 (2.5) 33.5 (11.8) -4.073 <0.001*
SEQ Social 25.8 (4.4) 48.2 (12.8) -6.045 <0.001*
SEQ Non-social 83.3 (12.8) 142.6 (41.8) -4.938 <0.001*
BPQ-VSF 61.8 (22.6) 70.1 (18.1) -1.078 0.29
Mean difference in heartbeat
counts 20.9 (14.4) 29.5 (30.9) -1.018 0.317
AQC 2-factor 5.3 (4.3) 10.9 (4.4) -3.537 <0.001*
† Pearson �2 test statistic and p-value.
Note: t-tests comparisons between TD and ASD groups. AQ = Autism Spectrum Quotient; SEQ = Sensory
Experiences Questionnaire version 3.0; Hyper = hypersensitivity; Hypo = hyposensitivity; BPQ-VSF = Body
Perception Questionnaire - Very Short Form; AQC = Alexithymia Questionnaire for Children.
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Figure 17. A. Correlation plot (lower triangle) of main behavioral variables in both groups and
both conditions combined. B. Correlations of wrongness ratings with behavioral variables in
both groups and both conditions combined. C. Correlations of punishment ratings with
behavioral variables in both groups and both conditions combined. D. Correlations of
permissibility ratings with behavioral variables in both groups and both conditions combined.
DS = Disgust sensitivity; DP = Disgust propensity; SEQ = Sensory Experiences Questionnaire
version 3.0; Hyper = hypersensitivity; Hypo = hyposensitivity; AQ = Autism Spectrum Quotient;
BPQ = Body Perception Questionnaire - Very Short Form; Mean HB diff = Mean absolute
mismatch in heartbeat counting task; AQC = Alexithymia Questionnaire for Children.
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4.3.2. Comparison of survey ratings between TD and ASD participants across odor conditions
For wrongness ratings, collapsing odor and non-odor conditions, the vignette type
(F=134.28, p<0.001, generalized partial η2
=0.699) was the only significant estimator of
differences across groups (TD, ASD) and conditions (smell, no smell). In separate analyses
within the TD and ASD groups, we found that vignette type was still the only significant
estimator of differences in wrongness ratings (TD: F=78.81, p<0.001, generalized partial
η2
=0.719; ASD: F=58.12, p<0.001, generalized partial η2
=0.684). Multiple comparisons testing
between vignette types in both TD and ASD groups revealed that wrongness ratings differed
significantly between all types (p<0.01), except between moral and purity violations (to be
expected, since purity vignettes are a subset of moral vignettes). For both groups, participants
rated that moral/purity violations were the most wrong, followed by the neutral negative
vignettes then physically disgusting vignettes (Moral/Purity>Neutral negative>Physically
disgusting; see Figure 18), thus satisfying part of our subordinate hypothesis. Both TD and ASD
groups, collapsing conditions, significantly differed from each other for only moral (p=0.017)
and purity violations (p=0.031), and not for physically disgusting and neutral negative vignettes
(see Table 13) satisfying part of our first primary hypothesis.
For punishment ratings of moral and purity vignettes, collapsing odor and non-odor
conditions, clinical group (F=13.65, p=0.001, generalized partial η2
=0.315) and vignette type
(F=8.624, p=0.007, generalized partial η2
=0.04) were significant estimators of differences across
participants. In separate analyses within the TD and ASD groups, we found that vignette type
was the only estimator of significant differences in punishment ratings in TD (F=6.933, p=0.019,
generalized partial η2
=0.053), but not in ASD (see Figure 18). Thus, our subordinate hypothesis
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only held true for our TD group. Also, no other significant estimators were found for the ASD
group (for comparisons between conditions. Both TD and ASD groups, collapsing conditions,
significantly differed from each other for moral (p<0.001) and purity violations (p=0.002). No
outliers were found for punishment ratings (see Table 13), thus satisfying part of our first
primary hypothesis.
For permissibility ratings of moral and purity vignettes, collapsing odor and non-odor
condition, no significant estimator of differences were found across groups and within the TD
and ASD groups. Both TD and ASD groups, collapsing conditions, did not significantly differ
from each other for either moral and purity violations (see Table 13). Both our first primary and
subordinate hypotheses did not hold true for permissibility ratings.
Table 13. Group comparisons on survey responses of perceived wrongness, deserved
punishment, and permissibility collapsed across smell and no smell conditions
Group Sig.
TD ASD U-statistic p-value
Wrongness
Moral violations 4.8 (0.7) 5.3 (0.5) 2.369 0.017*
Purity violations 4.7 (0.7) 5.3 (0.5) 2.151 0.031*
Physically disgusting scenes 1.7 (1.2) 2.2 (1.5) 0.817 0.432
Neutral negative scenes 2.2 (1.2) 2.8 (1.3) 0.921 0.363
Punishment
Moral violations 2.4 (0.6) 3.1 (0.5) 3.452 <0.001*
Purity violations 2.1 (0.7) 3.0 (0.6) 3.007 0.002*
Permissibility
Moral violations 5.1 (0.8) 5.4 (0.5) 0.923 0.363
Purity violations 5.0 (0.9) 5.4 (0.5) 1.310 0.198
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Figure 18. Distribution of the survey responses comparison the odor (blue) and non-odor (red)
conditions on perceived wrongness (A, D), necessary punishment (B, E), and permissibility (C,
F) of actions across the different vignette types in the TD (A to C) and the ASD (D to F) groups.
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4.3.3. Comparison of purity violations ratings between participants accounting for potential
covariates
Since disgust propensity (DP) was the only disgust trait to significantly differ between
groups, we prioritized DP as a covariate of interest, in separate models with gustatory sensitivity,
interoception and alexithymia as covariates. Our models suggested that DP, rather than DS (DS
models can be found in Appendix iii), was a more robust estimator of differences in moral
decision-making under disgust priming in the ASD group. This robustness of the sensoriemotional bias pertained when adjusting for sensory sensitivity, interoception, and alexithymia,
thus satisfying our second and third primary hypotheses:
1. DP and gustatory sensitivity
For wrongness ratings, DP significantly estimated the differences in wrongness ratings of
purity violations (F=5.271, p=0.032, generalized partial η2
=0.193) across participants. In
separate analyses within the TD and ASD groups, we found that DP (F=17.12, p=0.003,
generalized partial η2
=0.682) and the odor condition (F=11.412, p=0.01, generalized partial
η2
=0.588) significantly estimated the differences in wrongness ratings of purity violations in the
ASD group while adjusting for gustatory sensitivity, while no significant estimator was found for
the TD group.
For punishment ratings of purity vignettes, the clinical group (F=5.24, p=0.031,
generalized partial η2
=0.179) significantly estimated differences across participants while
adjusting for gustatory sensitivity. In separate analyses within the TD and ASD groups, we found
no significant estimators for punishment ratings.
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For permissibility ratings of purity vignettes, no significant estimator of differences were
found across groups. In separate analyses within the TD and ASD groups, we found that DP
(F=5.785, p=0.04, generalized partial η2
=0.391) and the odor condition (F=9.982, p=0.012,
generalized partial η2
=0.526) significantly estimated the differences in permissibility ratings of
purity violations in the ASD group while adjusting for gustatory sensitivity, while no significant
estimator was found for the TD group.
2. DP and interoception (heartbeat counting)
For wrongness ratings, DP significantly estimated the differences in wrongness ratings of
purity violations (F=4.885, p=0.038, generalized partial η2
=0.182) across participants. In
separate analyses within the TD and ASD groups, we found that DP (F=12.92, p=0.007,
generalized partial η2
=0.618) and the odor condition (F=31.06, p<0.001, generalized partial
η2
=0.795) significantly estimated the differences in wrongness ratings of purity violations in the
ASD group while adjusting for mean absolute difference in heartbeat counting/interoception,
while no significant estimator was found for the TD group.
For punishment ratings of purity vignettes, the clinical group (F=6.047, p=0.022,
generalized partial η2
=0.201) significantly estimated differences across participants while
adjusting for mean absolute difference in interoception. In separate analyses within the TD and
ASD groups, we found no significant estimators for punishment ratings.
For permissibility ratings of purity vignettes, interoception (F=4.842, p=0.038,
generalized partial η2
=0.174) significantly estimated differences across participants. In separate
analyses within the TD and ASD groups, we found that the odor condition (F=17.27, p=0.002,
generalized partial η2
=0.657) significantly estimated the differences in permissibility ratings of
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purity violations in the ASD group while adjusting for mean absolute difference in interoception
(F=6.732, p=0.029, generalized partial η2
=0.428), while no significant estimator was found for
the TD group.
3. DP and alexithymia
For wrongness ratings, DP significantly estimated the differences in wrongness ratings of
purity violations (F=7.907, p=0.01, generalized partial η2
=0.264) across participants. In separate
analyses within the TD and ASD groups, we found that DP (F=15.48, p=0.004, generalized
partial η2
=0.659) and the odor condition (F=17.31, p=0.003, generalized partial η2
=0.684)
significantly estimated the differences in wrongness ratings of purity violations in the ASD
group while adjusting for alexithymia, while no significant estimator was found for the TD
group.
For punishment ratings of purity vignettes, the clinical group (F=5.164, p=0.032,
generalized partial η2
=0.177) significantly estimated differences across participants while
adjusting for alexithymia. In separate analyses within the TD and ASD groups, we found no
significant estimators for punishment ratings.
For permissibility ratings of purity vignettes, no significant estimator of differences were
found across groups. In separate analyses within the TD and ASD groups, we found that the odor
condition (F=6.123, p=0.035, generalized partial η2
=0.405) significantly estimated the
differences in permissibility ratings of purity violations in the ASD group while adjusting for
alexithymia, while no significant estimator was found for the TD group.
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4.4. Discussion
Our investigation examined the influence of disgust processing on moral decision-making
in ASD and TD youth. First, in line with our first primary hypotheses, we find that autistic
individuals give significantly harsher moral judgments than non-autistic youth and also have
significantly stronger disgust propensity. Importantly, in line with our second primary
hypothesis, we found that in autism, physically being disgusted impacted the perceived
wrongness of purity violations. Notably, in satisfaction of our third primary hypothesis, while
adjusting for potential covariates, disgust propensity (DP) underlies the influence of physical
disgust processing on felt wrongness of purity violations in autism. However, no such influence
was found for non-autistics in these instances.
Particularly, in line with our third primary hypothesis, within autism, DP underlies the
influence of physical disgust processing on the permissibility/allowability of purity violations
after adjusting for gustatory hypersensitivity. Further in line with our subordinate hypothesis, all
participants rated higher for the moral and purity transgressions.
Intriguingly, these patterns provide valuable insights into the nuanced relationship
between individual differences in sensory sensitivity and disgust proneness, particularly in
autistic youth, and highlight their impact on embodied feelings of moral disgust. Specifically, in
line with research in non-autistics, we find evidence that disgust processing of physically
disgusting stimuli modulates the sensori-emotional bias in moral evaluations, especially in
situations involving violation of purity.
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4.4.1. Disgust priming influences purity violation moral ratings in ASD
Acts violating purity, encompassing degrading behaviors like actions posing
contamination risks have shown a robust association with the emotion of disgust (Dempsey et al.,
2022; Horberg et al., 2009, 2011). The findings underscore the link between purity violations and
the emotional response of disgust within the context of moral judgments. As a result, for both
perceived wrongness and permissibility ratings of the purity violations and after adjusting for
potential covariates (disgust proneness) and confounds (sensory sensitivity, alexithymia,
interoception), we found that ASD participants that performed the experiment within an odorprimed environment had significantly higher ratings for the morally disgusting scenarios as
compared to those participants who were not odor-primed. To the best of our knowledge, this is
the first study to show the influence of a disgusting odor on moral decision-making in autism.
Drawing from previous research in non-autistics, studies have shown that being exposed
to external foul smells results in harsher moral judgments (Schnall, Haidt, et al., 2008; Tracy et
al., 2019; Wheatley & Haidt, 2005). Additionally, other studies involving non-autistic
individuals also indicate that encountering physically repugnant stimuli can exert a considerable
impact on moral judgment when evaluating the behavior of moral actors. (Inbar & Pizarro, 2022;
Schnall, Benton, et al., 2008; Tracy et al., 2019; Wicker et al., 2003). Given the potential
differences in factors that influence moral decision-making, like disgust processing (Kalyva et
al., 2010), interoception ability (Butera et al., 2023; Butera et al., 2022; DuBois et al., 2016; Mul
et al., 2018), and the heightened prevalence of co-occurring alexithymia (Milosavljevic et al.,
2016; Vaiouli et al., 2022), one can anticipate a interplay of these elements within the autism
spectrum (Mul et al., 2018; Oakley et al., 2016). Thus, after adjustment for potential covariants
(interoception, sensory hypersensitivity, alexithymia), our findings were robust and underscored
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a multifaceted relationship, similar to that seen in non-autistics, between disgust processing
differences and moral decision-making in autism, shaped by disgust proneness.
4.4.2. Relationship between behavioral variability and moral ratings
We found that autistic youth reported significantly higher levels of DP and a trend of
higher disgust sensitivity (DS) than their TD counterparts. Contrary to existing literature
suggesting lower contamination sensitivity in autistic youth and an associated increased risk of
inappropriate contamination-related behaviors (Dimopoulou et al., 2006; Kalyva et al., 2010),
our findings present a different perspective in line with our previous research (Jayashankar et al.,
under review). In our previous investigation comparing autistic and non-autistic groups in terms
of disgust proneness, we uncovered a consistent trend of higher reported disgust proneness in
autistic individuals (Jayashankar et al., under review). These distinctions may be attributed to
variations in the assessment of disgust proneness, demographic factors including age groups, IQ
ranges, and sex ratios, as well as disparities in interoceptive awareness, alexithymia, and sensory
sensitivities. Moreover, disgust proneness (DS and DP) exhibited noteworthy positive
correlations with sensory sensitivity, the severity of autistic traits, and alexithymic traits across
both groups. This aligns with earlier research highlighting the impact of sensory sensitivity on
feelings of disgust and food selectivity (Cermak et al., 2010; Chow et al., 2022; Schmitt et al.,
2008).
Across and within the ASD group, we found that DS and DP were also significantly
associated with wrongness and punishment ratings of morality and purity violation vignettes, but
not the ratings for physically disgusting and neutral negative vignettes. This is in line with prior
research in non-autistic populations, suggesting that the interplay of disgust proneness and
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feelings associated with moral violations influences the decision-making process when
evaluating immoral scenarios (Chapman et al., 2009; Chapman & Anderson, 2014; Olatunji,
Puncochar, et al., 2016; Wagemans et al., 2018). Here, we find a similar pattern in autism,
indicating that the moral ratings of the ASD participants are similarly influenced by disgust as
the ratings of non-autistics, thus allowing the comparison of moral ratings across groups. This
may suggest that the mechanism of moral decision-making between autistic individuals and nonautistic peers are similar (Dempsey et al., 2022).
4.4.3. Significant differences in participant ratings for each vignette type
Across groups and within both the TD and ASD groups, we found that the moral/purity
violations were the most wrong and that the physically disgusting vignettes were rated as the
least wrong. This comparison shows that, accounting for clinical group and odor condition, the
moral and purity violation vignettes (Clifford et al., 2015), on average, elicit strong moral
feelings. Additionally, while neutral negative vignettes had significantly higher ratings than
physically disgusting vignettes, the average wrongness rating for both types indicate that these
vignettes elicit low moral feelings. Thus, the differences in wrongness ratings support our
hypothesis that moral decision-making differences would be seen primarily for moral violations.
Furthermore, across conditions, punishment ratings were significantly higher in the ASD
group as compared to the TD group. This may indicate that our autistic participants attribute
harsher punishments for moral and purity violations as they felt these impure acts illustrated in
the vignettes were significantly more wrong than other vignettes (Dempsey et al., 2022). Given
the prevalence of alexithymia in autistic individuals (Milosavljevic et al., 2016; Vaiouli et al.,
2022), alexithymic traits pose another factor that potentially explains the higher attributions of
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punishment in the ASD group. Previous research has shown that, instead of autistic traits,
alexithymic traits predispose autistic individuals towards more outcome-based moral decisions
(Patil et al., 2016) and such outcome-based decision-making in autism significantly influence
attributions of harsher judgments for both intentional and unintentional transgressions (Bellesi et
al., 2018). Additionally, the moral violation vignettes presented in this study did not include
explicit descriptions of the intentions of the moral actor, and may have further influenced the
differences in punishment attributions (Margoni et al., 2019).
4.4.4. Limitations
In this study, we employed various self-report measures, including DPSS-R for disgust
proneness, AQC for alexithymia, and BPQ for interoceptive awareness. Notably, approximately
50% of individuals with autism have alexithymia (Cuve et al., 2022), potentially impacting the
accuracy of self-reported disgust proneness. Similar concerns about self-report reliability have
been identified in the BPQ (Butera et al., 2022; Garfinkel et al., 2016). This research on autism
has underscored discrepancies in self-reports of interoceptive awareness (Suzman et al., 2021).
These differences are influenced by the ability to internally time events, such as synchronizing
heartbeats with a visual cue. Notably, non-autistic participants outperformed autistic participants
by four times in this aspect (Noel et al., 2018). Likewise, alexithymia, impacting the recognition
and expression of emotions like disgust, may introduce a response bias on disgust measures,
particularly in participants with more pronounced alexithymic traits (Hogeveen & Grafman,
2021; Mul et al., 2018). Therefore, further exploration of the influence of alexithymia on selfreports of emotional traits is necessary to assess their reliability in individuals facing challenges
in identifying and communicating emotions. For interoception, to resolve the issue faced by self-
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reported BPQ, we performed the heartbeat counting task to collect interoceptive awareness data
through observations rather than self- or parent-reports.
This study has limitations, including the sample sizes suitable for parametric statistical
comparisons. Additionally, autism is a heterogeneous neurodevelopmental condition (Masi et al.,
2017), and our study focused solely on high-functioning autistic youth, exhibiting a male bias in
our sample (only one female ASD participant). Future research should involve larger, more
diverse sample sizes to enhance understanding of the relationship between disgust proneness
traits and the differences in moral decision-making, encompassing greater diversity in terms of
IQ, sex, disgust processing, and alexithymia. Additionally, our protocol did not exhibit the same
effect of the odor condition, as seen in the ASD group, in our TD group. As previous research
has documented this relationship in non-autistics (Schnall, Haidt, et al., 2008; Tracy et al., 2019;
Wheatley & Haidt, 2005), our findings run contrary to the extant literature. We attribute this
discrepancy potentially to methodological caveats related to the presentation of the odor in the
testing environment, wherein all participants may not have been exposed to the same degree of
disgusting odor, and the individual differences in socio-cultural and sensory habituation in the
TD group, factors that we did not control for given the small sample size. Thus, further
investigation of this relationship of physical disgust processing and moral decision-making
should take efforts to ensure these limitations are accounted for.
4.5. Conclusion
Our study uncovered several key insights into the relationship between embodied feelings of
disgust and moral decisions in both non-autistic and autistic youth. Consistent with our prior
study, we found higher levels of disgust proneness in autistic youth compared to non-autistic
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counterparts. Wrongness and permissibility ratings of purity violations in autism were
particularly influenced by disgust propensity and odor priming, which highlighted the nuanced
influence of sensory sensitivity and disgust proneness on moral decision-making. Notably, the
study demonstrated that participant ratings for each vignette type revealed strong moral feelings
toward moral/purity violations, with harsher punishments attributed in autism. The novel insight
into the influence of disgust priming on purity violation moral ratings in autism emphasized the
link between purity violations, physical disgust and socio-moral disgust. This relationship may
have implications for everyday situations for autistic youth, as the sensori-emotional bias on
moral judgments could be elicited by naturally-occurring physically disgusting stimuli and affect
social communication and relationships. Overall, our research provides insight into the
multidimensional relationship between sensory and disgust sensitivity and their impact on
embodied feelings of moral disgust, particularly in autistic youth.
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CHAPTER 5: GRAND DISCUSSION
“Neuroanatomy isn't destiny. Neither is genetics. They don't define who you will be. But they do
define who you might be. They define who you can be.” (Grandin & Panek, 2013, p. 242)
This dissertation investigated the interplay between disgust , sensory, and moral
processing, along with autism symptomatology and neural functioning. Specifically, we
conducted behavioral and neuroimaging studies in typically developing (TD) and autistic (ASD)
youth in a series of studies. Taken together, they are the first set of studies to reveal that autistic
youth display differences in disgust processing both behaviorally and neuronally, and these
differences are related to different symptomatologies and may impact moral decision making.
Specifically in Chapter 2, we explored differences in ASD compared to TD in: 1) disgust
propensity and sensitivity (together known as disgust proneness); 2) functional brain activity
when experiencing physical and social disgust. The results indicated that autistic youth exhibit
heightened disgust sensitivity and propensity, which are intricately linked to sensory processing
and alexithymic traits. Moreover, fMRI imaging during the experience of physical disgust
revealed in the ASD group, altered responses in emotion-related brain regions, such as the midinsula (MI), ventromedial prefrontal cortex (vmPFC), and medial orbitofrontal cortex (mOFC).
These neural activity patterns correlated with individual variations in disgust proneness implying
the role of these regions in modulating disgust responses. During social disgust processing, the
ASD group also showed altered neural responses in the MI, mOFC, anterior cingulate cortex
(ACC). Overall, Chapter 2 underscored that autistic youth behaviorally display differences in
disgust processing and differential neural patterns during disgust processing.
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In Chapter 3, we explored potential neural connectivity differences autistic youth during
disgust processing. Notably, I found that ASD youth exhibit hypoconnectivity between disgust
processing regions, including the dorsal anterior insula (dAI), mid-insula (MI), and ventromedial
prefrontal cortex (vmPFC). Connectivity differences between the dAI and MI correlated with
individual variations in disgust sensitivity and propensity (together known as disgust proneness),
indicating potential implications for inappropriate eating behaviors such as pica and other foodrelated behaviors in ASD. Conversely, hyperconnectivity is observed between the amygdala and
right MI during disgust processing in ASD youth, potentially compensating for reduced
amygdala activity (see Figure 7). Moreover, higher disgust sensitivity and propensity traits
correlated with connectivity between the right MI and amygdala, suggesting an influence on
attentional biases and potential compensatory mechanisms and differences in social disgust
behavior. Overall, Chapter 3 provided valuable insights into the neural connectivity
underpinnings of disgust processing network differences in ASD, underscoring the importance of
both hypo- and hyperconnectivity patterns in understanding the complexity of disgust processing
in this population.
In Chapter 4, we investigated the interplay between physical disgust processing and
moral decision-making in autistic youth in comparison to TD youth, offering exploratory insights
into the underlying influence of disgust processing on moral decisions. Notably, the behavioral
study revealed that exposure to a disgusting odor significantly heightened ratings of wrongness
and permissibility for purity violations in ASD youth, highlighting the potential influence of
disgust processing on moral judgments in autism. Furthermore, regardless of the smell of the
room, ASD youth tend to assign harsher punishments for moral and purity violations, potentially
influenced by alexithymic traits and outcome-based decision-making tendencies. This influence
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is modulated by various factors, including disgust proneness, interoceptive awareness, and
alexithymia, underscoring the complexity of factors shaping moral decision-making in autistic
youth. Further, in the ASD group, disgust proneness correlated positively with sensory
sensitivity, and autistic and alexithymic traits, highlighting the multifaceted nature of factors
potentially influencing moral evaluations. Overall, Chapter 4 resulted in a novel experimental
demonstration of the interplay between physical disgust processing and moral choices in autistic
youth, emphasizing the importance of considering individual differences in sensory sensitivity
and disgust traits when examining moral judgments.
Together, these studies provide a comprehensive understanding of the multifaceted
relationship between disgust processing, autistic traits, and food-related and sociomoral
behavioral differences in autistic youth. Here, we summarize the key conclusions from the three
studies and their implications for future research and intervention strategies.
5.1. Elevated disgust traits in autistic youth
Across all samples in this dissertation, one common theme that we identified within the
autistic participant cohorts was the heightened self-reported ratings of disgust sensitivity and
disgust propensity (together known as disgust proneness) as compared to their non-autistic
counterparts. Interestingly, such findings of elevated disgust traits run contrary to extant
literature (see Introduction; Jayashankar & Aziz-Zadeh, 2023). Previous research on disgust and
contamination-related behaviors have revealed lower contamination sensitivity in autistic youth,
which they suggest as an indication of increased risk of inappropriate food and contaminationrelated behaviors in autism (Dimopoulou et al., 2006; Kalyva et al., 2010). In fact, autistic youth
are more likely at risk of eating inedible items, or pica behaviors (Fields et al., 2021; Matson et
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al., 2009). While further studies are needed to determine the relationship between these latter
behaviors and disgust processing, it seems likely that latter behaviors would be related to
decreased disgust sensitivity. Nevertheless, our results consistently found the opposite pattern
within our autistic youth participants. To the best of our knowledge, these studies were the first
to show increased disgust sensitivities in autism. The difference between our study and prior
studies may stem from a number of factors, including: differences in measurement of disgust
proneness, demographics including age groups, IQ ranges, sex ratios, or differences in
interoceptive awareness, alexithymia, and sensory sensitivities.
Our data also indicated a significant positive relationship between sensory sensitivities
and disgust proneness in autism. It is possible that in autism, higher sensory sensitivities may
predispose youth to higher sensitivity in disgusting experiences. This is especially important
given the high incidence of sensory sensitivities in ASD (around 53-94%; Kirby et al., 2022).
Both reduced (hyposensitivity) and heightened (hypersensitivity) sensitivity to odors have been
observed in autism, with variations possibly tied to age (Ashwin et al., 2014; Kumazaki et al.,
2016, 2019; Muratori et al., 2017; Wicker et al., 2016). Particularly, sensitivities to smells and
tastes may significantly impact the eating behaviors and disgust reactivity of autistic children
(Chistol et al., 2018; Luisier et al., 2015). However, the exact relationship between sensory
sensitivities in smell or taste and increased feelings of disgust remains uncertain; these
sensitivities may simply evoke greater discomfort rather than specifically triggering disgust.
Additionally, tactile sensitivities correlate with increased aversion to specific food items, and
characteristics linked to sensitivity to textures forecast eating behaviors and selectivity in
children with autism. Notably, there is a tendency to avoid mushy, soft textures while favoring
crunchy, hard textures (Baraskewich et al., 2021; Coulthard et al., 2022; Martins & Pliner, 2006;
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Schmitt et al., 2008). Therefore, a better understanding of the relationship between disgust
processing and the spectrum of sensory sensitivities, and how this interplay may influence
appropriate eating behaviors is needed.
One factor that may influence results in different studies is factors related to self-reports.
For instance, about 50% of individuals diagnosed with autism exhibit alexithymia, potentially
affecting the reliability of self-reported levels of disgust proneness (Cuve et al., 2022). Similar
concerns regarding the reliability of self-reporting have been raised in studies of self-reported
interoceptive awareness (Butera et al., 2022; Garfinkel et al., 2016). Recent research in autism
has highlighted discrepancies in self-reported interoceptive awareness (Suzman et al., 2021),
revealing that despite self-reports, neurotypical individuals outperform those with autism on
interoception tasks (Noel et al., 2018). Consequently, autistic individuals might report no
difficulties with interoception on self-report measures despite significant differences in
performance. Similarly, alexithymia, which affects the recognition and communication of
emotions such as disgust, could introduce a response bias during disgust assessments,
particularly in individuals with more pronounced alexithymic traits (Hogeveen & Grafman,
2021; Mul et al., 2018). Hence, further investigation is warranted into the impact of alexithymia
on self-reports of emotional traits, as well as the correlation between observed performance on
interoceptive awareness tasks and disgust processing.
Altogether, we note that while our results indicate increased disgust proneness in ASD
youth, further studies are needed to better understand how these may relate to increased pica
behaviors, and increased sample sizes are needed to better understand potential heterogeneity in
this factor in ASD (Dimopoulou et al., 2006; Kalyva et al., 2010). Future studies should keep
these caveats in mind while exploring the associations of neural functioning with self-reported
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behavioral constructs, especially those constructs (interoception, affect) that autistic individuals
have difficulty expressing and experiencing. Additionally, due to the prevalence of food
selectivity challenges in autism and the extensive sensory behavior heterogeneity within the
autism population (Masi et al., 2017), it is imperative for further research studies to grasp the
potential mediation or influence of the range of sensory behavior on disgust processing.
5.2. Mid-insula as a possible hub for disgust-related processing
Contrary to previous literature that attributed emotion processing (Chang et al., 2013),
including disgust processing, to the ventral anterior insula (AI), this dissertation found that
physical and social disgust-related processing was localized within the right mid-insula (MI). In
fact, previous research has visually represented disgust-related insular neural patterns and
suggests that disgust processing activity is localized within both the ventral AI and MI regions
during physical and social disgust contexts (Vicario et al., 2017). Furthermore, we found that
feelings of disgust negatively modulate MI activity in both autistic and non-autistic groups, when
viewing disgusting foods and disgust facial expressions. Given that our sample of autistic youth
exhibit higher disgust sensitivities, we believe that the relationship between MI and disgust traits
suggests that as sensitivities become more typical (as observed in typical developing individuals;
Wicker et al., 2003), there might also be an increase in brain activity in the MI.
In addition, when comparing neural connectivity between groups, in autism, MI activity
was associated with reduced disgust-related activity within the dorsal AI and ventromedial
prefrontal cortex when observing disgusting foods, and increased disgust-related activity in the
amygdala when observing disgust facial expressions in autism. Such hypoconnectivity may
suggest underlying downstream differences in communication with attention and executive
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functioning regions that adversely affect appropriate disgust behaviors (Deen et al., 2011).
Additionally, hypoconnectivity between the MI and vmPFC may indicate potential dysfunction
in neural circuitry important for communicating affective valence of physically disgusting
stimuli, which could hinder downstream emotion regulation processes (Chen et al., 2021; South
& Rodgers, 2017).
Converse to the latter findings, during the observation of disgust facial expressions, we
found hyperconnectivity between the MI and amygdala in autism. Prior research has shown that
when observing emotional facial expressions, autistic youth potentially experience attentional
biases, such as initial hypervigilance followed by avoidant behavior towards the faces (Zhao et
al., 2016). The amygdala is also more active and connected in autism when individuals are
processing implicit threat information after observing emotional facial expressions (Chen et al.,
2021). In fact, it is this implicit threat processing that robustly predicts attentional biases to
disgusting stimuli (J. S. Green & Teachman, 2013). Thus, we suggest the observed
hyperconnectivity with the amygdala could reflect heightened implicit threat processing in
autistic youth, leading to the previously observed attentional biases to disgust facial expressions
(Zhao et al., 2016).
Altogether, our findings suggest that difficulties in disgust processing in the autistic
youth stem from differential activity and connectivity patterns of the MI. As a region most
closely associated with chemosensation (Uddin et al., 2017), the MI may be responsible for early
processing and evaluation of physical and social disgust-related stimuli, relaying information
downstream to emotion regulation and decision-making systems. Future studies should consider
the MI as a distinct region of interest and explore the dynamics of disgust processing within its
connectome. For instance, naturalistic functional scanning paradigms and data analysis (for
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example, inter-subject correlation) would be beneficial to further exploring the unique activity
and connectivity patterns associated with disgust experiences in autistic youth.
5.3. Moral disgust decision-making is influenced by physical disgust in autism
Our most notable finding from the final behavioral study (Chapter 4) was that when
autistic youth were in a room with disgusting odor, they made harsher moral judgments of others
behavior (as compared to autistic youth in a neutral odor room). This bias was particularly
prominent in purity or sanctity moral violations (e.g., peeing in a public swimming pool). This is
in line with our hypotheses, as previous research has shown that such degrading acts pose a
potential contamination risk (physical or social) and have a robust association with disgust
(Dempsey et al., 2022; Horberg et al., 2009, 2011). These findings, which were robust after
accounting for other behavioral covariates (sensory sensitivity, alexithymia, and interoception),
further highlight the connection between purity transgressions and disgust processing traits. To
our knowledge, this is the first study to demonstrate the impact of a physically disgusting odor
on moral decision-making in autistic youth.
These latter results build on prior research conducted on non-autistic individuals (Schnall,
Haidt, et al., 2008; Tracy et al., 2019; Wheatley & Haidt, 2005) demonstrating that exposure to
physically disgusting odors leads to more severe moral judgments in autistic youth as well.
Moreover, studies involving non-autistic participants have also suggested that encountering
generally physically repulsive stimuli significantly influences moral judgment regarding the
actions of moral agents (Inbar & Pizarro, 2022; Liuzza et al., 2019; Schnall, Benton, et al., 2008;
Tracy et al., 2019). Consequently, given the observed sensori-emotional bias in autism upon
exposure to a disgusting odor, our findings highlight a similar complex association between
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differences in disgust processing and moral decision-making in autism. In autism, such disgustinduced harsher moral judgments may interact with with coexisting intention understanding
difficulties (Bellesi et al., 2018; Fadda et al., 2016; Margoni & Surian, 2016), and resulting in
much harsher judgments for unintentional purity violations.
5.4. An updated model of altered disgust processing in autism
In the introduction of this dissertation, we emphasized a potential model for disgust,
sensory, and socio-emotional processing in autism. This proposed model brings together
modified models of the somatic marker hypothesis (Damasio, 2003; Damasio et al., 2000),
intention understanding, and emotion processing (Jayashankar & Aziz-Zadeh, 2023).
Here, we present updated versions of this conceptual model to include key findings from
the studies in this dissertation, emphasizing the data found in autism (Figures 19 & 20). Based on
neural activity patterns we observed (see Chapter 2, Jayashankar et al., under review), we
separate the activity in the mid-insula and orbitofrontal cortices to highlight their relationship
with disgust processing in autism. In particular, we highlight the disgust-related hypoactivations
in the right MI and right mOFC (blue boxes) and the disgust-related hyperactivity in the left
mOFC (red box). Further, we highlight the hypoconnectivity (see Chapter 3) in autism (in blue
arrows, see Figure 19) observed between the right MI with the anterior cingulate cortex (ACC),
dorsal AI, vmPFC; the left and right mOFC with the dorsal AI; and the right mOFC with the
right amygdala. Additionally, we present the influence of disgust processing differences in
autism on activity in the MI and mOFC regions, as well as the connection between the right MI
and dorsal AI. Further, we highlight the hyperconnectivity (see Chapter 3) in autism (in red
arrows, see Figure 20) observed between the right MI with the mid-cingulate cortex (MCC),
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ventral AI, right amygdala; the left and right mOFC with the striatum; and the right mOFC with
the left amygdala. In addition to the activity in the MI and mOFC regions, we present the
influence of disgust processing differences on the connection between the right MI and right
amygdala. Altogether, these patterns indicate differences in the disgust processing neural
network that potentially explains the physical and social disgust behavioral differences observed
in autistic youth (Dimopoulou et al., 2006; Kalyva et al., 2010; Katarzyna et al., 2010; Monk et
al., 2010; Siegal et al., 2011; Yeung et al., 2020; Zhao et al., 2016). Since most of these findings
are novel, further research replicating our studies are required to corroborate these identified
patterns. Yet, we believe that these models provide a significant contribution to the knowledge
base and will help in the development of future research endeavors and the design of behavioral
interventions.
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Figure 19. Updated hypoconnected conceptual model of the relationship between disgust,
sensory and moral processing regions in the brain and the observed activation and connectivity
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patterns in Autism Spectrum disorder (ASD) and the effect of ASD symptomatology on these
functional connections. Hypoconnectivity links are shown in blue arrows with triangular tips,
lower disgust-related activations (TD>ASD) in right mid-insula and right medial orbitofrontal
cortex (mOFC) in ASD are shown in a blue block, higher disgust-related activations (ASD>TD)
in the left mOFC in ASD are shown in a red block, and the effect of ASD symptoms are shown in
orange dashed arrows with rounded tips. AI, anterior insula; OFC, orbitofrontal cortex; ACC,
anterior cingulate cortex; MCC, middle cingulate cortex; VMPFC, ventromedial prefrontal
cortex; DLPFC, dorsolateral prefrontal cortex; PCC, posterior cingulate cortex; SMA,
supplementary motor area; Physical, activity/connection during physical disgust; Social;
activity/connection during social disgust; Both; activity/connection during both physical and
social disgust. Adapted from somatic marker hypothesis models in Bechara, 2013; Koob, Arends,
McCracken, & Le Moal, 2019; Saive, Royet, & Plailly, 2014.
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Figure 20. Updated hyperconnected conceptual model of the relationship between disgust,
sensory and moral processing regions in the brain and the observed activation and connectivity
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patterns in Autism Spectrum disorder (ASD) and the effect of ASD symptomatology on these
functional connections. Hyperconnectivity links are shown in red arrows with triangular tips,
lower disgust-related activations (TD>ASD) in right mid-insula and right medial orbitofrontal
cortex (mOFC) in ASD are shown in a blue block, higher disgust-related activations (ASD>TD)
in the left mOFC in ASD are shown in a red block, and the effect of ASD symptoms are shown in
orange dashed arrows with rounded tips. AI, anterior insula; OFC, orbitofrontal cortex; ACC,
anterior cingulate cortex; MCC, middle cingulate cortex; VMPFC, ventromedial prefrontal
cortex; DLPFC, dorsolateral prefrontal cortex; PCC, posterior cingulate cortex; SMA,
supplementary motor area; Physical, activity/connection during physical disgust; Social;
activity/connection during social disgust; Both; activity/connection during both physical and
social disgust. Adapted from somatic marker hypothesis models in Bechara, 2013; Koob, Arends,
McCracken, & Le Moal, 2019; Saive, Royet, & Plailly, 2014.
5.5. Occupational science lens for disgust processing research in autism
In occupational therapy (OT) and occupational science (OS), occupation has had many
definitions. For the purpose of this dissertation, we drew inspiration from previous work that
defined occupations as actions that fully involve the individual, are meaningful to the individual,
and result in either a tangible or intangible product (Schkade & Schultz, 2003), as well as from a
more recent conceptualization from Wilcock and Townsend (2014) which defined occupation as
“what people want, need, or have to do” (p. 31, McKay, 2020). In essence, occupations are
actions performed by humans that help one manage life experiences, adapt to their physical and
social environments, engage in social interactions, and enable personal growth (Bruner, 1990;
Canadian Association of Occupational Therapists & Townsend, 2002; Gray, 1997; Nelson, 1997;
Wilcock, 1991). Additionally, engaging in these meaningful actions has long been associated
with physical and mental health benefits (Clark et al., 2014; Jackson et al., 1998; Reilly, 1962,
1966), as well as finding purpose, success, social connectedness and improving one’s own wellbeing (Hammell & Iwama, 2012; Hasselkus, 2011; Holahan, 2014; Leufstadius et al., 2008). We
believe these definitions capture the essence of occupations associated with the current research,
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particularly food-related and social engagement occupations that could be affected by differences
in disgust responses, potentially leading to unfavorable outcomes.
One of the original mandates of OS established the discipline as a basic science to
produce a knowledge base in support of evidence-based practices in OT (Clark et al., 1991;
Yerxa, 1990). Since then, OS has evolved into an interdisciplinary and hybrid field (Calhoun,
2021), drawing knowledge from other scientific domains to better understand occupation and the
implications of different systems on suitable outcomes (Clark et al., 1991). We employed tools
from neuroscience and psychology to develop a knowledge foundation to help explore how
occupation can be used to promote better physical and social health outcomes by intervening in
differential functioning in autism. In this way, we used an occupational science lens to approach
our study of disgust processing in autism and the occupations that are potentially affected by
differences in physical, social or moral disgust in autism. Our pragmatic OS perspective
(Boisvert, 1998; Cutchin, 2004; Cutchin & Dickie, 2012) informed our exploration of the
mechanisms by which the body actively experienced disgust emotion states, understood the
disgust feelings of others, and the implications of differences in autism on social belonging
(Hitch et al., 2014b, 2014a; Wilcock, 2002) and transactions between the individual child and
their social environment. Future research on disgust in autism using the OS perspective may
study and consider all aspects of the interdependent relationship between the autistic child,
occupations and their environment to expand on this body of work and before making
ameliorative decisions.
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5.6. Limitations, Future directions & Conclusion
The studies discussed offer valuable insights into the relationship between disgust
processing with neural function and moral decision-making in autism. However, they are
constrained by few limitations that merit consideration. Firstly, the prevalence of alexithymia
among autistic individuals (Cuve et al., 2022), affecting approximately half of the population,
poses a challenge. Alexithymia can influence self-reports of disgust proneness and emotional
processing, potentially biasing the results and complicating the understanding of the relationship
between disgust sensitivity and other variables. Addressing this limitation requires the
development and implementation of strategies to account for alexithymia's influence, such as
incorporating objective measures of emotional processing in future studies.
Secondly, the skewed sex ratios and lower mean IQ within the studied groups may limit
the generalizability of the findings (Zeidan et al., 2022). The predominance of males in the ASD
sample and the lower mean IQ of this group compared to TD controls may introduce biases and
hinder the ability to draw conclusions applicable to the broader population of individuals with
ASD. As prior autism studies used similarly-sized samples (Albajara Sáenz et al., 2020;
Caeyenberghs et al., 2016; Gonzalez-Gadea et al., 2016; Kangarani-Farahani et al., 2022), the
samples used in our fMRI studies were suitable for our exploratory analyses. Nevertheless, to
address this limitation, future research should prioritize recruiting larger and more diverse
samples, including individuals with varying levels of functioning and increased numbers of
females, to enhance the generalizability and applicability of the findings (Marek et al., 2022;
Masi et al., 2017).
In addition, the moral disgust study had some methodological limitations. Inadequate
control of individual differences in socio-cultural and sensory habituation (Chapman &
170
Anderson, 2012; Ille et al., 2017; Poppa & Bechara, 2018), particularly regarding the exposure to
disgusting odors, may confound the results and undermine the validity of the conclusions drawn.
For example, some cultures may have more tolerance (and less disgust) for body-related odors
than other cultures (Graça da Silva, 2021). To mitigate this limitation, more standardized
protocols and rigorous control measures should be implemented in future research to ensure the
reliability of findings and minimize potential confounds. This is essential for the expansion of
the conceptual model, through the use of neuroimaging in tandem with standardized protocols to
unravel relationships within the disgust emotion network associated with moral disgust
processing.
Moreover, future research could explore additional factors that may influence disgust
proneness and its neural underpinnings in ASD. Investigating the impact of comorbid conditions,
such as anxiety or the spectrum of sensory processing differences, could provide further insights
into the variability observed in disgust responses among autistic youth. Longitudinal studies
tracking changes in disgust processing and neural activity over time could also elucidate
developmental trajectories and inform potential intervention strategies tailored to the unique
needs of autistic youth. By addressing these limitations and pursuing these future directions,
researchers can develop a more nuanced understanding of the interplay between disgust
processing, sensory sensitivity, and autism symptomatology, with implications for both research
and clinical practice.
In conclusion, by elucidating the underlying mechanisms influencing behavioral
manifestations of psychological constructs associated with the disgust emotion, this dissertation
contributes to the broader literature on cognitive and affective processes in autism, paving the
171
way for future research and interventions aimed at enhancing the social and emotional wellbeing of autistic individuals.
********************************************
172
BIBLIOGRAPHY
Acker, M. (2009). Breast is Best…But Not Everywhere: Ambivalent Sexism and Attitudes
Toward Private and Public Breastfeeding. Sex Roles, 61(7), 476–490.
https://doi.org/10.1007/s11199-009-9655-z
Adolfi, F., Couto, B., Richter, F., Decety, J., Lopez, J., Sigman, M., Manes, F., & Ibáñez, A.
(2017). Convergence of interoception, emotion, and social cognition: A twofold fMRI
meta-analysis and lesion approach. Cortex, 88, 124–142.
https://doi.org/10.1016/j.cortex.2016.12.019
Adolphs, R. (2002). Neural systems for recognizing emotion. Current Opinion in Neurobiology,
12(2), 169–177. https://doi.org/10.1016/S0959-4388(02)00301-X
Adolphs, R., Tranel, D., & Damasio, A. R. (2003). Dissociable neural systems for recognizing
emotions. Brain and Cognition, 52(1), 61–69. https://doi.org/10.1016/S0278-
2626(03)00009-5
Adolphs, R., Tranel, D., Hamann, S., Young, A. W., Calder, A. J., Phelps, E. A., Anderson, A.,
Lee, G. P., & Damasio, A. R. (1999). Recognition of facial emotion in nine individuals
with bilateral amygdala damage. Neuropsychologia, 37(10), 1111–1117.
https://doi.org/10.1016/s0028-3932(99)00039-1
Alanazi, F. S., Powell, P. A., & Power, M. J. (2018). Depression as a disorder of disgust. In The
Revolting Self (pp. 151–165). Routledge.
Alaoui-Ismaïli, O., Robin, O., Rada, H., Dittmar, A., & Vernet-Maury, E. (1997). Basic
Emotions Evoked by Odorants: Comparison Between Autonomic Responses and SelfEvaluation. Physiology & Behavior, 62(4), 713–720. https://doi.org/10.1016/S0031-
9384(97)90016-0
Albajara Sáenz, A., Van Schuerbeek, P., Baijot, S., Septier, M., Deconinck, N., Defresne, P.,
Delvenne, V., Passeri, G., Raeymaekers, H., Slama, H., Victoor, L., Willaye, E.,
Peigneux, P., Villemonteix, T., & Massat, I. (2020). Disorder-specific brain volumetric
abnormalities in Attention-Deficit/Hyperactivity Disorder relative to Autism Spectrum
Disorder. PLoS ONE, 15(11), e0241856. https://doi.org/10.1371/journal.pone.0241856
Allison, C., Auyeung, B., & Baron-Cohen, S. (2012). Toward Brief “Red Flags” for Autism
Screening: The Short Autism Spectrum Quotient and the Short Quantitative Checklist in
1,000 Cases and 3,000 Controls. Journal of the American Academy of Child &
Adolescent Psychiatry, 51(2), 202-212.e7. https://doi.org/10.1016/j.jaac.2011.11.003
American Psychiatric Association. (2013). Diagnostic and statistical manual of mental
disorders: DSM-5TM, 5th ed (pp. xliv, 947). American Psychiatric Publishing, Inc.
https://doi.org/10.1176/appi.books.9780890425596
Anderson, J. S., Druzgal, T. J., Froehlich, A., DuBray, M. B., Lange, N., Alexander, A. L.,
Abildskov, T., Nielsen, J. A., Cariello, A. N., Cooperrider, J. R., Bigler, E. D., &
173
Lainhart, J. E. (2011). Decreased interhemispheric functional connectivity in autism.
Cerebral Cortex (New York, N.Y.: 1991), 21(5), 1134–1146.
https://doi.org/10.1093/cercor/bhq190
Anderson, J. S., Nielsen, J. A., Froehlich, A. L., DuBray, M. B., Druzgal, T. J., Cariello, A. N.,
Cooperrider, J. R., Zielinski, B. A., Ravichandran, C., Fletcher, P. T., Alexander, A. L.,
Bigler, E. D., Lange, N., & Lainhart, J. E. (2011). Functional connectivity magnetic
resonance imaging classification of autism. Brain, 134(12), 3742–3754.
https://doi.org/10.1093/brain/awr263
Anderson, S. W., Bechara, A., Damasio, H., Tranel, D., & Damasio, A. R. (1999). Impairment of
social and moral behavior related to early damage in human prefrontal cortex. Nature
Neuroscience, 2(11), 1032–1037. https://doi.org/10.1038/14833
Andreou, M., & Skrimpa, V. (2020). Theory of Mind Deficits and Neurophysiological
Operations in Autism Spectrum Disorders: A Review. Brain Sciences, 10(6), Article 6.
https://doi.org/10.3390/brainsci10060393
Angyal, A. (1941). Disgust and related aversions. The Journal of Abnormal and Social
Psychology, 36(3), 393–412. https://doi.org/10.1037/h0058254
Appadurai, A. (1981). Gastro-politics in Hindu South Asia. American Ethnologist, 8(3), 494–
511. https://doi.org/10.1525/ae.1981.8.3.02a00050
Armstrong, T., & Olatunji, B. O. (2017). Pavlovian disgust conditioning as a model for
contamination-based OCD: Evidence from an analogue study. Behaviour Research and
Therapy, 93, 78–87. https://doi.org/10.1016/j.brat.2017.03.009
Armstrong, T., Sarawgi, S., & Olatunji, B. O. (2012). Attentional bias toward threat in
contamination fear: Overt components and behavioral correlates. Journal of Abnormal
Psychology, 121(1), 232–237. https://doi.org/10.1037/a0024453
Ashwin, C., Baron-Cohen, S., Wheelwright, S., O’Riordan, M., & Bullmore, E. T. (2007).
Differential activation of the amygdala and the ‘social brain’ during fearful faceprocessing in Asperger Syndrome. Neuropsychologia, 45(1), 2–14.
https://doi.org/10.1016/j.neuropsychologia.2006.04.014
Ashwin, C., Chapman, E., Howells, J., Rhydderch, D., Walker, I., & Baron-Cohen, S. (2014).
Enhanced olfactory sensitivity in autism spectrum conditions. Molecular Autism, 5(1),
53. https://doi.org/10.1186/2040-2392-5-53
Askew, C., Çakır, K., Põldsam, L., & Reynolds, G. (2014). The effect of disgust and fear
modeling on children’s disgust and fear for animals. Journal of Abnormal Psychology,
123(3), Article 3. http://dx.doi.org/10.1037/a0037228
Ausderau, K. K., & Baranek, G. T. (2013). Sensory Experiences Questionnaire. In F. R. Volkmar
(Ed.), Encyclopedia of Autism Spectrum Disorders (pp. 2770–2774). Springer.
https://doi.org/10.1007/978-1-4419-1698-3_1192
174
Ausderau, K., Sideris, J., Furlong, M., Little, L. M., Bulluck, J., & Baranek, G. T. (2014).
National Survey of Sensory Features in Children with ASD: Factor Structure of the
Sensory Experience Questionnaire (3.0). Journal of Autism and Developmental
Disorders, 44(4), 915–925. https://doi.org/10.1007/s10803-013-1945-1
Auyeung, B., Baron-Cohen, S., Wheelwright, S., & Allison, C. (2008). The Autism Spectrum
Quotient: Children’s Version (AQ-Child). Journal of Autism and Developmental
Disorders, 38(7), 1230–1240. https://doi.org/10.1007/s10803-007-0504-z
Azlan, H. A., Overton, P. G., Simpson, J., & Powell, P. A. (2017). Differential disgust
responding in people with cancer and implications for psychological wellbeing.
Psychology & Health, 32(1), 19–37. https://doi.org/10.1080/08870446.2016.1235165
Bai, C., Wang, Y., Zhang, Y., Wang, X., Chen, Z., Yu, W., Zhang, H., Li, X., Zhu, K., Wang,
Y., & Zhang, T. (2023). Abnormal gray matter volume and functional connectivity
patterns in social cognition-related brain regions of young children with autism spectrum
disorder. Autism Research, 16(6), 1124–1137. https://doi.org/10.1002/aur.2936
Baranek, G. T., David, F. J., Poe, M. D., Stone, W. L., & Watson, L. R. (2006). Sensory
Experiences Questionnaire: Discriminating sensory features in young children with
autism, developmental delays, and typical development. Journal of Child Psychology and
Psychiatry, 47(6), 591–601. https://doi.org/10.1111/j.1469-7610.2005.01546.x
Baraskewich, J., von Ranson, K. M., McCrimmon, A., & McMorris, C. A. (2021). Feeding and
eating problems in children and adolescents with autism: A scoping review. Autism,
25(6), 1505–1519. https://doi.org/10.1177/1362361321995631
Baron-Cohen, S., Hoekstra, R. A., Knickmeyer, R., & Wheelwright, S. (2006). The AutismSpectrum Quotient (AQ)—Adolescent Version. Journal of Autism and Developmental
Disorders, 36(3), 343–350. https://doi.org/10.1007/s10803-006-0073-6
Baron-Cohen, S., Leslie, A. M., & Frith, U. (1985). Does the autistic child have a “theory of
mind” ? Cognition, 21(1), 37–46. https://doi.org/10.1016/0010-0277(85)90022-8
Barrett, H. C., Bolyanatz, A., Crittenden, A. N., Fessler, D. M. T., Fitzpatrick, S., Gurven, M.,
Henrich, J., Kanovsky, M., Kushnick, G., Pisor, A., Scelza, B. A., Stich, S., von Rueden,
C., Zhao, W., & Laurence, S. (2016). Small-scale societies exhibit fundamental variation
in the role of intentions in moral judgment. Proceedings of the National Academy of
Sciences, 113(17), 4688–4693.
Barrett, L. F., Mesquita, B., Ochsner, K. N., & Gross, J. J. (2006). The Experience of Emotion.
Annual Review of Psychology, 58(1), 373–403.
https://doi.org/10.1146/annurev.psych.58.110405.085709
Barrett, L. F., & Simmons, W. K. (2015). Interoceptive predictions in the brain. Nature Reviews.
Neuroscience, 16(7), 419–429. https://doi.org/10.1038/nrn3950
Bastiaansen, J. A., Thioux, M., Nanetti, L., van der Gaag, C., Ketelaars, C., Minderaa, R., &
175
Keysers, C. (2011). Age-Related Increase in Inferior Frontal Gyrus Activity and Social
Functioning in Autism Spectrum Disorder. Biological Psychiatry, 69(9), 832–838.
https://doi.org/10.1016/j.biopsych.2010.11.007
Beard, C., Rifkin, L. S., & Björgvinsson, T. (2017). Characteristics of interpretation bias and
relationship with suicidality in a psychiatric hospital sample. Journal of Affective
Disorders, 207, 321–326. https://doi.org/10.1016/j.jad.2016.09.021
Bechara, A. (2013). Chapter 35—The Neural Basis of Decision Making in Addiction. In P. M.
Miller (Ed.), Biological Research on Addiction (pp. 341–352). Academic Press.
https://doi.org/10.1016/B978-0-12-398335-0.00035-2
Bechara, A., & Damasio, A. R. (2005). The somatic marker hypothesis: A neural theory of
economic decision. Games and Economic Behavior, 52(2), 336–372.
https://doi.org/10.1016/j.geb.2004.06.010
Beck, A. T. (2008). The evolution of the cognitive model of depression and its neurobiological
correlates. The American Journal of Psychiatry, 165(8), 969–977.
https://doi.org/10.1176/appi.ajp.2008.08050721
Belfi, A. M., Koscik, T. R., & Tranel, D. (2015). Damage to the insula is associated with
abnormal interpersonal trust. Neuropsychologia, 71, 165–172.
https://doi.org/10.1016/j.neuropsychologia.2015.04.003
Bellesi, G., Vyas, K., Jameel, L., & Channon, S. (2018). Moral reasoning about everyday
situations in adults with autism spectrum disorder. Research in Autism Spectrum
Disorders, 52, 1–11. https://doi.org/10.1016/j.rasd.2018.04.009
Bird, G., Silani, G., Brindley, R., White, S., Frith, U., & Singer, T. (2010). Empathic brain
responses in insula are modulated by levels of alexithymia but not autism. Brain, 133(5),
1515–1525. https://doi.org/10.1093/brain/awq060
Bloom, P. (2009). Descartes’ Baby: How the Science of Child Development Explains What
Makes Us Human. Basic Books.
Boisvert, R. D. (1998). John Dewey: Rethinking Our Time. SUNY Press.
Bookheimer, S. Y., Wang, A. T., Scott, A., Sigman, M., & Dapretto, M. (2008). Frontal
contributions to face processing differences in autism: Evidence from fMRI of inverted
face processing. Journal of the International Neuropsychological Society: JINS, 14(6),
922–932. https://doi.org/10.1017/S135561770808140X
Borg, C., Bedoin, N., Peyron, R., Bogey, S., Laurent, B., & Thomas-Antérion, C. (2013).
Impaired emotional processing in a patient with a left posterior insula-SII lesion.
Neurocase, 19(6), 592–603. https://doi.org/10.1080/13554794.2012.713491
Borgomaneri, S., Gazzola, V., & Avenanti, A. (2015). Transcranial magnetic stimulation reveals
two functionally distinct stages of motor cortex involvement during perception of
176
emotional body language. Brain Structure and Function, 220(5), 2765–2781.
https://doi.org/10.1007/s00429-014-0825-6
Bos, J., & Stokes, M. A. (2019). Cognitive empathy moderates the relationship between affective
empathy and wellbeing in adolescents with autism spectrum disorder. European Journal
of Developmental Psychology, 16(4), 433–446.
https://doi.org/10.1080/17405629.2018.1444987
Brener, J., & Ring, C. (2016). Towards a psychophysics of interoceptive processes: The
measurement of heartbeat detection. Philosophical Transactions of the Royal Society B:
Biological Sciences, 371(1708), 20160015. https://doi.org/10.1098/rstb.2016.0015
Brown, A. (2018). 9 Sociological and Cultural Influences upon Breastfeeding. In Breastfeeding
and Breast Milk – from Biochemistry to Impact, (Ed, Family Larson- Rosenquist
Foundation) Georg Thieme Verlag KG. PubPub.
https://doi.org/10.21428/3d48c34a.2a0f254a
Bruner, J. S. (1990). Acts of Meaning: Four Lectures on Mind and Culture. Harvard University
Press.
Buon, M., Dupoux, E., Jacob, P., Chaste, P., Leboyer, M., & Zalla, T. (2013). The Role of
Causal and Intentional Judgments in Moral Reasoning in Individuals with High
Functioning Autism. Journal of Autism and Developmental Disorders, 43(2), 458–470.
https://doi.org/10.1007/s10803-012-1588-7
Butera, C. D., Harrison, L., Kilroy, E., Jayashankar, A., Shipkova, M., Pruyser, A., & AzizZadeh, L. (2023). Relationships between alexithymia, interoception, and emotional
empathy in autism spectrum disorder. Autism, 27(3), 690–703.
https://doi.org/10.1177/13623613221111310
Butera, C., Kaplan, J., Kilroy, E., Harrison, L., Jayashankar, A., Loureiro, F., & Aziz-Zadeh, L.
(2023). The relationship between alexithymia, interoception, and neural functional
connectivity during facial expression processing in autism spectrum disorder.
Neuropsychologia, 180, 108469. https://doi.org/10.1016/j.neuropsychologia.2023.108469
Cabrera, A., Kolacz, J., Pailhez, G., Bulbena-Cabre, A., Bulbena, A., & Porges, S. W. (2018).
Assessing body awareness and autonomic reactivity: Factor structure and psychometric
properties of the Body Perception Questionnaire-Short Form (BPQ-SF). International
Journal of Methods in Psychiatric Research, 27(2), e1596.
https://doi.org/10.1002/mpr.1596
Caeyenberghs, K., Taymans, T., Wilson, P. H., Vanderstraeten, G., Hosseini, H., & van
Waelvelde, H. (2016). Neural signature of developmental coordination disorder in the
structural connectome independent of comorbid autism. Developmental Science, 19(4),
599–612. https://doi.org/10.1111/desc.12424
Calder, A. J., Beaver, J. D., Davis, M. H., van Ditzhuijzen, J., Keane, J., & Lawrence, A. D.
(2007). Disgust sensitivity predicts the insula and pallidal response to pictures of
177
disgusting foods. The European Journal of Neuroscience, 25(11), 3422–3428.
https://doi.org/10.1111/j.1460-9568.2007.05604.x
Calder, A. J., Keane, J., Manes, F., Antoun, N., & Young, A. W. (2000). Impaired recognition
and experience of disgust following brain injury. Nature Neuroscience, 3(11), Article 11.
https://doi.org/10.1038/80586
Calder, A. J., Lawrence, A. D., & Young, A. W. (2001). Neuropsychology of fear and loathing.
Nature Reviews. Neuroscience, 2(5), 352–363. https://doi.org/10.1038/35072584
Calhoun, A. D. (2021). The development and future of occupational science: A budding
occupational scientist’s reflections and assertions about the discipline. Journal of
Occupational Science, 28(2), 193–207. https://doi.org/10.1080/14427591.2020.1801492
Cameron, C. D., Payne, B. K., & Doris, J. M. (2013). Morality in high definition: Emotion
differentiation calibrates the influence of incidental disgust on moral judgments. Journal
of Experimental Social Psychology, 49(4), 719–725.
https://doi.org/10.1016/j.jesp.2013.02.014
Cameron, C. D., Reber, J., Spring, V. L., & Tranel, D. (2018). Damage to the ventromedial
prefrontal cortex is associated with impairments in both spontaneous and deliberative
moral judgments. Neuropsychologia, 111, 261–268.
https://doi.org/10.1016/j.neuropsychologia.2018.01.038
Canadian Association of Occupational Therapists, & Townsend, E. (2002). Enabling occupation:
An occupational therapy perspective. Canadian Association of Occupational Therapists.
Cannon, P. R., Schnall, S., & White, M. (2011). Transgressions and Expressions: Affective
Facial Muscle Activity Predicts Moral Judgments. Social Psychological and Personality
Science, 2(3), 325–331. https://doi.org/10.1177/1948550610390525
Capponi, I., & Roland, F. (2021). Relationship between emotional labelling of breastfeeding
situation and intention to breastfeed/support breastfeeding among French adolescents and
young people. Journal of Public Health, 29(1), 135–144. https://doi.org/10.1007/s10389-
019-01037-9
Caria, A., & de Falco, S. (2015). Anterior insular cortex regulation in autism spectrum disorders.
Frontiers in Behavioral Neuroscience, 9, 38. https://doi.org/10.3389/fnbeh.2015.00038
Carlson, N. R. (2012). Physiology of behavior. Pearson Higher Ed.
Carpenter, M. B. (1991). Core Text of Neuroanatomy. Williams & Wilkins.
Casagrande, M., Boncompagni, I., Forte, G., Guarino, A., & Favieri, F. (2020). Emotion and
overeating behavior: Effects of alexithymia and emotional regulation on overweight and
obesity. Eating and Weight Disorders - Studies on Anorexia, Bulimia and Obesity, 25(5),
1333–1345. https://doi.org/10.1007/s40519-019-00767-9
178
Cauda, F., Costa, T., Torta, D. M. E., Sacco, K., D’Agata, F., Duca, S., Geminiani, G., Fox, P.
T., & Vercelli, A. (2012). Meta-analytic clustering of the insular cortex: Characterizing
the meta-analytic connectivity of the insula when involved in active tasks. NeuroImage,
62(1), 343–355. https://doi.org/10.1016/j.neuroimage.2012.04.012
Cauda, F., D’Agata, F., Sacco, K., Duca, S., Geminiani, G., & Vercelli, A. (2011). Functional
connectivity of the insula in the resting brain. NeuroImage, 55(1), 8–23.
https://doi.org/10.1016/j.neuroimage.2010.11.049
Cavanagh, K., & Davey, G. (2000). The development of a measure of individual differences in
disgust. British Psychology Society, Winchester, UK.
Cerliani, L., Thomas, R. M., Jbabdi, S., Siero, J. C. W., Nanetti, L., Crippa, A., Gazzola, V.,
D’Arceuil, H., & Keysers, C. (2012). Probabilistic tractography recovers a rostrocaudal
trajectory of connectivity variability in the human insular cortex. Human Brain Mapping,
33(9), 2005–2034. https://doi.org/10.1002/hbm.21338
Cermak, S. A., Curtin, C., & Bandini, L. G. (2010). Food Selectivity and Sensory Sensitivity in
Children with Autism Spectrum Disorders. Journal of the American Dietetic Association,
110(2), 238–246. https://doi.org/10.1016/j.jada.2009.10.032
Chang, L. J., Yarkoni, T., Khaw, M. W., & Sanfey, A. G. (2013). Decoding the Role of the
Insula in Human Cognition: Functional Parcellation and Large-Scale Reverse Inference.
Cerebral Cortex, 23(3), 739–749. https://doi.org/10.1093/cercor/bhs065
Chapman, H. A., & Anderson, A. K. (2012). Understanding disgust. Annals of the New York
Academy of Sciences, 1251(1), 62–76. https://doi.org/10.1111/j.1749-6632.2011.06369.x
Chapman, H. A., & Anderson, A. K. (2013). Things rank and gross in nature: A review and
synthesis of moral disgust. Psychological Bulletin, 139(2), 300–327.
https://doi.org/10.1037/a0030964
Chapman, H. A., & Anderson, A. K. (2014). Trait physical disgust is related to moral judgments
outside of the purity domain. Emotion (Washington, D.C.), 14(2), 341–348.
https://doi.org/10.1037/a0035120
Chapman, H. A., Kim, D. A., Susskind, J. M., & Anderson, A. K. (2009). In Bad Taste:
Evidence for the Oral Origins of Moral Disgust. Science, 323(5918), 1222–1226.
https://doi.org/10.1126/science.1165565
Chau, A., Zhong, W., Gordon, B., Krueger, F., & Grafman, J. (2018). Anterior insula lesions and
alexithymia reduce the endorsements of everyday altruistic attitudes. Neuropsychologia,
117, 428–439. https://doi.org/10.1016/j.neuropsychologia.2018.07.002
Chen, Y.-C., Chen, C., Martínez, R. M., Fan, Y.-T., Liu, C.-C., Chen, C.-Y., & Cheng, Y.
(2021). An amygdala-centered hyper-connectivity signature of threatening face
processing predicts anxiety in youths with autism spectrum conditions. Autism Research,
14(11), 2287–2299. https://doi.org/10.1002/aur.2595
179
Chistol, L. T., Bandini, L. G., Must, A., Phillips, S., Cermak, S. A., & Curtin, C. (2018). Sensory
Sensitivity and Food Selectivity in Children with Autism Spectrum Disorder. Journal of
Autism and Developmental Disorders, 48(2), 583–591. https://doi.org/10.1007/s10803-
017-3340-9
Chow, C. Y., Skouw, S., Bech, A. C., Olsen, A., & Bredie, W. L. P. (2022). A review on
children’s oral texture perception and preferences in foods. Critical Reviews in Food
Science and Nutrition, 0(0), 1–19. https://doi.org/10.1080/10408398.2022.2136619
Cisler, J. M., & Koster, E. H. W. (2010). Mechanisms of attentional biases towards threat in
anxiety disorders: An integrative review. Clinical Psychology Review, 30(2), 203–216.
https://doi.org/10.1016/j.cpr.2009.11.003
Clark, F., Jackson, J., & Pyatak, E. (2014). Developing an integrated occupational science
research program: The USC Well Elderly and Pressure Ulcer Prevention studies.
Occupational Science for Occupational Therapy. Thorofare, NJ: SLACK Incorporated.
Clark, F., Parham, D., Carlson, M. E., Frank, G., Jackson, J., Pierce, D., Wolfe, R. J., & Zemke,
R. (1991). Occupational Science: Academic Innovation in the Service of Occupational
Therapy’s Future. American Journal of Occupational Therapy, 45(4), 300–310.
https://doi.org/10.5014/ajot.45.4.300
Clifford, S., Iyengar, V., Cabeza, R., & Sinnott-Armstrong, W. (2015). Moral foundations
vignettes: A standardized stimulus database of scenarios based on moral foundations
theory. Behavior Research Methods, 47(4), 1178–1198. https://doi.org/10.3758/s13428-
014-0551-2
Cloutman, L. L., Binney, R. J., Drakesmith, M., Parker, G. J. M., & Lambon Ralph, M. A.
(2012). The variation of function across the human insula mirrors its patterns of structural
connectivity: Evidence from in vivo probabilistic tractography. NeuroImage, 59(4),
3514–3521. https://doi.org/10.1016/j.neuroimage.2011.11.016
Cochran, D., Fallon, D., Hill, M., & Frazier, J. A. (2013). The role of oxytocin in psychiatric
disorders: A review of biological and therapeutic research findings. Harvard Review of
Psychiatry, 21(5), 219–247. https://doi.org/10.1097/HRP.0b013e3182a75b7d
Cohen, D., Hoshino‐Browne, E., & Leung, A. K. ‐y. (2007). Culture and the Structure of
Personal Experience: Insider and Outsider Phenomenologies of the Self and Social
World. In Advances in Experimental Social Psychology (Vol. 39, pp. 1–67). Academic
Press. https://doi.org/10.1016/S0065-2601(06)39001-6
Conners, C. K., Pitkanen, J., & Rzepa, S. R. (2011). Conners 3rd Edition (Conners 3; Conners
2008). In J. S. Kreutzer, J. DeLuca, & B. Caplan (Eds.), Encyclopedia of Clinical
Neuropsychology (pp. 675–678). Springer. https://doi.org/10.1007/978-0-387-79948-
3_1534
Cook, R., Brewer, R., Shah, P., & Bird, G. (2013). Alexithymia, not autism, predicts poor
recognition of emotional facial expressions. Psychological Science, 24(5), 723–732.
180
https://doi.org/10.1177/0956797612463582
Corbett, B. A., Carmean, V., Ravizza, S., Wendelken, C., Henry, M. L., Carter, C., & Rivera, S.
M. (2009). A functional and structural study of emotion and face processing in children
with autism. Psychiatry Research, 173(3), 196–205.
https://doi.org/10.1016/j.pscychresns.2008.08.005
Coulthard, H., Abdullahi, N., Bell, K., & Noon, E. (2022). Understanding disgust-based food
rejection in picky and non-picky eaters: Willingness to touch and taste familiar foods
with changes. Food Quality and Preference, 97, 104442.
https://doi.org/10.1016/j.foodqual.2021.104442
Couto, B., Sedeño, L., Sposato, L. A., Sigman, M., Riccio, P. M., Salles, A., Lopez, V.,
Schroeder, J., Manes, F., & Ibanez, A. (2013). Insular networks for emotional processing
and social cognition: Comparison of two case reports with either cortical or subcortical
involvement. Cortex, 49(5), 1420–1434. https://doi.org/10.1016/j.cortex.2012.08.006
Craig, A. D. (2002). How do you feel? Interoception: the sense of the physiological condition of
the body. Nature Reviews Neuroscience, 3(8), Article 8. https://doi.org/10.1038/nrn894
Craig, A. D. (2003). Interoception: The sense of the physiological condition of the body. Current
Opinion in Neurobiology, 13(4), 500–505. https://doi.org/10.1016/s0959-4388(03)00090-
4
Craig, A. D. (2009). How do you feel--now? The anterior insula and human awareness. Nature
Reviews. Neuroscience, 10(1), 59–70. https://doi.org/10.1038/nrn2555
Curtis, G. C., & Thyer, B. A. (1983). Fainting on exposure to phobic stimuli. The American
Journal of Psychiatry, 140(6), 771–774. https://doi.org/10.1176/ajp.140.6.771
Curtis, V. (2011). Why disgust matters. Philosophical Transactions of the Royal Society B:
Biological Sciences, 366(1583), 3478–3490. https://doi.org/10.1098/rstb.2011.0165
Curtis, V., Aunger, R., & Rabie, T. (2004). Evidence that disgust evolved to protect from risk of
disease. Proceedings of the Royal Society of London. Series B: Biological Sciences,
271(suppl_4), S131–S133. https://doi.org/10.1098/rsbl.2003.0144
Curtis, V., & Biran, A. (2001). Dirt, Disgust, and Disease: Is Hygiene in Our Genes?
Perspectives in Biology and Medicine, 44(1), 17–31.
https://doi.org/10.1353/pbm.2001.0001
Cutchin, M. P. (2004). Using Deweyan Philosophy To Rename and Reframe Adaptation-toEnvironment. American Journal of Occupational Therapy, 58(3), 303–312.
https://doi.org/10.5014/ajot.58.3.303
Cutchin, M. P., & Dickie, V. A. (2012). Transactional Perspectives on Occupation. Springer
Science & Business Media.
181
Cuve, H. C., Murphy, J., Hobson, H., Ichijo, E., Catmur, C., & Bird, G. (2022). Are Autistic and
Alexithymic Traits Distinct? A Factor-Analytic and Network Approach. Journal of
Autism and Developmental Disorders, 52(5), 2019–2034. https://doi.org/10.1007/s10803-
021-05094-6
Dal Monte, O., Krueger, F., Solomon, J. M., Schintu, S., Knutson, K. M., Strenziok, M., Pardini,
M., Leopold, A., Raymont, V., & Grafman, J. (2013). A voxel-based lesion study on
facial emotion recognition after penetrating brain injury. Social Cognitive and Affective
Neuroscience, 8(6), 632–639. https://doi.org/10.1093/scan/nss041
Dale, A., & Anderson, D. (1978). Information variables in voluntary control and classical
conditioning of heart rate: Field dependence and heart-rate perception. Perceptual and
Motor Skills, 47(1), 79–85. https://doi.org/10.2466/pms.1978.47.1.79
Damasio, A. (2003). Feelings of emotion and the self. Annals of the New York Academy of
Sciences, 1001, 253–261. https://doi.org/10.1196/annals.1279.014
Damasio, A. (2008). Descartes’ Error: Emotion, Reason and the Human Brain. Random House.
Damasio, A. (2011). Neural basis of emotions. Scholarpedia, 6(3), 1804.
https://doi.org/10.4249/scholarpedia.1804
Damasio, A. R., Grabowski, T. J., Bechara, A., Damasio, H., Ponto, L. L. B., Parvizi, J., &
Hichwa, R. D. (2000). Subcortical and cortical brain activity during the feeling of selfgenerated emotions. Nature Neuroscience, 3(10), Article 10.
https://doi.org/10.1038/79871
Danovitch, J., & Bloom, P. (2009). Children’s extension of disgust to physical and moral events.
Emotion, 9(1), 107–112. https://doi.org/10.1037/a0014113
Dapretto, M., Davies, M. S., Pfeifer, J. H., Scott, A. A., Sigman, M., Bookheimer, S. Y., &
Iacoboni, M. (2006). Understanding emotions in others: Mirror neuron dysfunction in
children with autism spectrum disorders. Nature Neuroscience, 9(1), 28–30.
https://doi.org/10.1038/nn1611
Darwin, C. (1872). The expression of the emotions in man and animal.
Darwin, C., & Prodger, P. (1998). The Expression of the Emotions in Man and Animals. Oxford
University Press.
Davey, G. C. L. (1994). Self-reported fears to common indigenous animals in an adult UK
population: The role of disgust sensitivity. British Journal of Psychology, 85(4), 541–
554. https://doi.org/10.1111/j.2044-8295.1994.tb02540.x
Davey, G. C. L. (1995). Preparedness and phobias: Specific evolved associations or a
generalized expectancy bias? Behavioral and Brain Sciences, 18(2), 289–325.
https://doi.org/10.1017/S0140525X00038498
182
Davey, G. C. L. (2011). Disgust: The disease-avoidance emotion and its dysfunctions.
Philosophical Transactions of the Royal Society B: Biological Sciences, 366(1583),
3453–3465. https://doi.org/10.1098/rstb.2011.0039
Davey, G. C. L., Bickerstaffe, S., & MacDonald, B. A. (2006). Experienced disgust causes a
negative interpretation bias: A causal role for disgust in anxious psychopathology.
Behaviour Research and Therapy, 44(10), 1375–1384.
https://doi.org/10.1016/j.brat.2005.10.006
Davidson, J. (2008). Autistic culture online: Virtual communication and cultural expression on
the spectrum. Social & Cultural Geography, 9(7), 791–806.
https://doi.org/10.1080/14649360802382586
Deen, B., Pitskel, N. B., & Pelphrey, K. A. (2011). Three systems of insular functional
connectivity identified with cluster analysis. Cerebral Cortex (New York, N.Y.: 1991),
21(7), 1498–1506. https://doi.org/10.1093/cercor/bhq186
Dell’Osso, L., Massoni, L., Battaglini, S., De Felice, C., Nardi, B., Amatori, G., Cremone, I. M.,
& Carpita, B. (2023). Emotional dysregulation as a part of the autism spectrum
continuum: A literature review from late childhood to adulthood. Frontiers in Psychiatry,
14, 1234518. https://doi.org/10.3389/fpsyt.2023.1234518
Dempsey, E. E., Moore, C., Johnson, S. A., Stewart, S. H., & Smith, I. M. (2020). Morality in
autism spectrum disorder: A systematic review. Development and Psychopathology,
32(3), 1069–1085. https://doi.org/10.1017/S0954579419001160
Dempsey, E. E., Moore, C., Johnson, S. A., Stewart, S. H., & Smith, I. M. (2022). Moral
Foundations Theory Among Autistic and Neurotypical Children. Frontiers in
Psychology, 12. https://www.frontiersin.org/article/10.3389/fpsyg.2021.782610
Di Martino, A., Ross, K., Uddin, L. Q., Sklar, A. B., Castellanos, F. X., & Milham, M. P. (2009).
Functional brain correlates of social and nonsocial processes in autism spectrum
disorders: An activation likelihood estimation meta-analysis. Biological Psychiatry,
65(1), 63–74. https://doi.org/10.1016/j.biopsych.2008.09.022
Dimopoulou, S., Kalyva, E., & Siegal, M. (2006). Autism, Communication Impairment, and
Gastrointestinal Symptoms. 256. http://seerc.org/docs/phdresources/DSC2006.pdf#page=264
Dixon, T. (2012). “Emotion”: The History of a Keyword in Crisis. Emotion Review, 4(4), 338–
344. https://doi.org/10.1177/1754073912445814
DuBois, D., Ameis, S. H., Lai, M.-C., Casanova, M. F., & Desarkar, P. (2016). Interoception in
Autism Spectrum Disorder: A review. International Journal of Developmental
Neuroscience, 52, 104–111. https://doi.org/10.1016/j.ijdevneu.2016.05.001
Duerden, E. G., Arsalidou, M., Lee, M., & Taylor, M. J. (2013). Lateralization of affective
processing in the insula. NeuroImage, 78, 159–175.
183
https://doi.org/10.1016/j.neuroimage.2013.04.014
Dziobek, I., Rogers, K., Fleck, S., Bahnemann, M., Heekeren, H. R., Wolf, O. T., & Convit, A.
(2008). Dissociation of cognitive and emotional empathy in adults with Asperger
syndrome using the Multifaceted Empathy Test (MET). Journal of Autism and
Developmental Disorders, 38(3), 464–473. https://doi.org/10.1007/s10803-007-0486-x
Ebisch, S. J. H., Gallese, V., Willems, R. M., Mantini, D., Groen, W. B., Romani, G. L.,
Buitelaar, J. K., & Bekkering, H. (2011). Altered intrinsic functional connectivity of
anterior and posterior insula regions in high-functioning participants with autism
spectrum disorder. Human Brain Mapping, 32(7), 1013–1028.
https://doi.org/10.1002/hbm.21085
Ekman, P. (1992). Are there basic emotions? Psychological Review, 99(3), 550–553.
https://doi.org/10.1037/0033-295X.99.3.550
Ekman, P., & Cordaro, D. (2011). What is Meant by Calling Emotions Basic. Emotion Review,
3(4), 364–370. https://doi.org/10.1177/1754073911410740
Ekman, P., Friesen, W. V., & Ellsworth, P. (2013). Emotion in the Human Face: Guidelines for
Research and an Integration of Findings. Elsevier.
Ekman, P., Levenson, R. W., & Friesen, W. V. (1983). Autonomic nervous system activity
distinguishes among emotions. Science, 221(4616), 1208–1210.
https://doi.org/10.1126/science.6612338
Elad-Strenger, J., Proch, J., & Kessler, T. (2020). Is Disgust a “Conservative” Emotion?
Personality and Social Psychology Bulletin, 46(6), 896–912.
https://doi.org/10.1177/0146167219880191
Enticott, P. G., Kennedy, H. A., Johnston, P. J., Rinehart, N. J., Tonge, B. J., Taffe, J. R., &
Fitzgerald, P. B. (2014). Emotion recognition of static and dynamic faces in autism
spectrum disorder. Cognition and Emotion, 28(6), 1110–1118.
https://doi.org/10.1080/02699931.2013.867832
Erickson, C. A., Stigler, K. A., Corkins, M. R., Posey, D. J., Fitzgerald, J. F., & McDougle, C. J.
(2005). Gastrointestinal factors in autistic disorder: A critical review. Journal of Autism
and Developmental Disorders, 35(6), 713–727. https://doi.org/10.1007/s10803-005-
0019-4
Eskine, K. J., Kacinik, N. A., & Prinz, J. J. (2011). A bad taste in the mouth: Gustatory disgust
influences moral judgment. Psychological Science, 22(3), 295–299.
https://doi.org/10.1177/0956797611398497
Everaert, J., Duyck, W., & Koster, E. H. W. (2014). Attention, interpretation, and memory biases
in subclinical depression: A proof-of-principle test of the combined cognitive biases
hypothesis. Emotion, 14(2), 331–340. https://doi.org/10.1037/a0035250
184
Fadda, R., Parisi, M., Ferretti, L., Saba, G., Foscoliano, M., Salvago, A., & Doneddu, G. (2016).
Exploring the Role of Theory of Mind in Moral Judgment: The Case of Children with
Autism Spectrum Disorder. Frontiers in Psychology, 7.
https://doi.org/10.3389/fpsyg.2016.00523
Farhoumandi, N., Mollaey, S., Heysieattalab, S., Zarean, M., & Eyvazpour, R. (2021). Facial
Emotion Recognition Predicts Alexithymia Using Machine Learning. Computational
Intelligence and Neuroscience, 2021, e2053795. https://doi.org/10.1155/2021/2053795
Farmer, C. A., & Aman, M. G. (2011). Aggressive behavior in a sample of children with autism
spectrum disorders. Research in Autism Spectrum Disorders, 5(1), 317–323.
https://doi.org/10.1016/j.rasd.2010.04.014
Fields, V. L., Soke, G. N., Reynolds, A., Tian, L. H., Wiggins, L., Maenner, M., DiGuiseppi, C.,
Kral, T. V. E., Hightshoe, K., & Schieve, L. A. (2021). Pica, Autism, and Other
Disabilities. Pediatrics, 147(2), e20200462. https://doi.org/10.1542/peds.2020-0462
Fleischman, D. S. (2014). Women’s Disgust Adaptations. In V. A. Weekes-Shackelford & T. K.
Shackelford (Eds.), Evolutionary Perspectives on Human Sexual Psychology and
Behavior (pp. 277–296). Springer. https://doi.org/10.1007/978-1-4939-0314-6_15
Foa, E. B., Franklin, M. E., Perry, K. J., & Herbert, J. D. (1996). Cognitive biases in generalized
social phobia. Journal of Abnormal Psychology, 105(3), 433–439.
https://doi.org/10.1037/0021-843X.105.3.433
Fontenelle, L. F., Frydman, I., Hoefle, S., Oliveira-Souza, R., Vigne, P., Bortolini, T. S., Suo, C.,
Yücel, M., Mattos, P., & Moll, J. (2018). Decoding moral emotions in obsessivecompulsive disorder. NeuroImage: Clinical, 19, 82–89.
https://doi.org/10.1016/j.nicl.2018.04.002
Fumagalli, M., & Priori, A. (2012). Functional and clinical neuroanatomy of morality. Brain,
135(7), 2006–2021. https://doi.org/10.1093/brain/awr334
Fusar-Poli, P., Placentino, A., Carletti, F., Landi, P., Allen, P., Surguladze, S., Benedetti, F.,
Abbamonte, M., Gasparotti, R., Barale, F., Perez, J., McGuire, P., & Politi, P. (2009).
Functional atlas of emotional faces processing: A voxel-based meta-analysis of 105
functional magnetic resonance imaging studies. Journal of Psychiatry & Neuroscience :
JPN, 34(6), 418–432. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2783433/
Gan, X., Zhou, X., Li, J., Jiao, G., Jiang, X., Biswal, B., Yao, S., Klugah-Brown, B., & Becker,
B. (2022). Common and distinct neurofunctional representations of core and social
disgust in the brain: Coordinate-based and network meta-analyses. Neuroscience &
Biobehavioral Reviews, 135, 104553. https://doi.org/10.1016/j.neubiorev.2022.104553
Garfinkel, S. N., Tiley, C., O’Keeffe, S., Harrison, N. A., Seth, A. K., & Critchley, H. D. (2016).
Discrepancies between dimensions of interoception in autism: Implications for emotion
and anxiety. Biological Psychology, 114, 117–126.
https://doi.org/10.1016/j.biopsycho.2015.12.003
185
Georgiadis, C., Schreck, M., Gervasio, M., Kemp, J., Freeman, J., Garcia, A., & Case, B. (2020).
Disgust propensity and sensitivity in childhood anxiety and obsessive-compulsive
disorder: Two constructs differentially related to obsessional content. Journal of Anxiety
Disorders, 76, 102294. https://doi.org/10.1016/j.janxdis.2020.102294
Gert, B., & Gert, J. (2020). The Definition of Morality. In E. N. Zalta (Ed.), The Stanford
Encyclopedia of Philosophy (Fall 2020). Metaphysics Research Lab, Stanford University.
https://plato.stanford.edu/archives/fall2020/entries/morality-definition/
Gilbert, P. (2015). Self-disgust, self-hatred, and compassion-focused therapy. The Revolting Self:
Perspectives on the Psychological, Social, and Clinical Implications of Self-Directed
Disgust, 223–242.
Giner-Sorolla, R., Kupfer, T., & Sabo, J. (2018). Chapter Five - What Makes Moral Disgust
Special? An Integrative Functional Review. In J. M. Olson (Ed.), Advances in
Experimental Social Psychology (Vol. 57, pp. 223–289). Academic Press.
https://doi.org/10.1016/bs.aesp.2017.10.001
Goldenberg, J. L., Pyszczynski, T., Greenberg, J., Solomon, S., Kluck, B., & Cornwell, R.
(2001). I am not an animal: Mortality salience, disgust, and the denial of human
creatureliness. Journal of Experimental Psychology: General, 130(3), 427–435.
https://doi.org/10.1037/0096-3445.130.3.427
Gonzalez-Gadea, M. L., Sigman, M., Rattazzi, A., Lavin, C., Rivera-Rei, A., Marino, J., Manes,
F., & Ibanez, A. (2016). Neural markers of social and monetary rewards in children with
Attention-Deficit/Hyperactivity Disorder and Autism Spectrum Disorder. Scientific
Reports, 6(1), Article 1. https://doi.org/10.1038/srep30588
Goodwin, M. S., Mazefsky, C. A., Ioannidis, S., Erdogmus, D., & Siegel, M. (2019). Predicting
aggression to others in youth with autism using a wearable biosensor. Autism Research,
12(8), 1286–1296. https://doi.org/10.1002/aur.2151
Graça da Silva, S. (2021). “You Stink!” Smell and Moralisation of the Other. In A. Falcato & S.
Graça da Silva (Eds.), The Politics of Emotional Shockwaves (pp. 147–163). Springer
International Publishing. https://doi.org/10.1007/978-3-030-56021-8_7
Graham, J., Haidt, J., Koleva, S., Motyl, M., Iyer, R., Wojcik, S. P., & Ditto, P. H. (2013).
Chapter Two - Moral Foundations Theory: The Pragmatic Validity of Moral Pluralism. In
P. Devine & A. Plant (Eds.), Advances in Experimental Social Psychology (Vol. 47, pp.
55–130). Academic Press. https://doi.org/10.1016/B978-0-12-407236-7.00002-4
Graham, J., Haidt, J., & Nosek, B. A. (2009). Liberals and conservatives rely on different sets of
moral foundations. Journal of Personality and Social Psychology, 96(5), 1029–1046.
https://doi.org/10.1037/a0015141
Grandin, T., & Panek, R. (2013). The Autistic Brain: Thinking Across the Spectrum. Houghton
Mifflin Harcourt.
186
Grant, C. M., Boucher, J., Riggs, K. J., & Grayson, A. (2005). Moral understanding in children
with autism. Autism, 9(3), 317–331. https://doi.org/10.1177/1362361305055418
Gray, J. M. (1997). Application of the phenomenological method to the concept of occupation.
Journal of Occupational Science, 4(1), 5–17.
https://doi.org/10.1080/14427591.1997.9686416
Gray, K., Young, L., & Waytz, A. (2012). Mind Perception Is the Essence of Morality.
Psychological Inquiry, 23(2), 101–124. https://doi.org/10.1080/1047840X.2012.651387
Green, J. S., & Teachman, B. A. (2013). Predictive Validity of Explicit and Implicit Threat
Overestimation in Contamination Fear. Journal of Obsessive-Compulsive and Related
Disorders, 2(1), 1–8. https://doi.org/10.1016/j.jocrd.2012.09.002
Green, S. A., Hernandez, L., Tottenham, N., Krasileva, K., Bookheimer, S. Y., & Dapretto, M.
(2015). Neurobiology of Sensory Overresponsivity in Youth With Autism Spectrum
Disorders. JAMA Psychiatry, 72(8), 778–786.
https://doi.org/10.1001/jamapsychiatry.2015.0737
Greene, J. D., & Paxton, J. M. (2009). Patterns of neural activity associated with honest and
dishonest moral decisions. Proceedings of the National Academy of Sciences, 106(30),
12506–12511. https://doi.org/10.1073/pnas.0900152106
Greening, S., Norton, L., Virani, K., Ty, A., Mitchell, D., & Finger, E. (2014). Individual
differences in the anterior insula are associated with the likelihood of financially helping
versus harming others. Cognitive, Affective & Behavioral Neuroscience, 14(1), 266–277.
https://doi.org/10.3758/s13415-013-0213-3
Greimel, E., Schulte-Rüther, M., Kircher, T., Kamp-Becker, I., Remschmidt, H., Fink, G. R.,
Herpertz-Dahlmann, B., & Konrad, K. (2010). Neural mechanisms of empathy in
adolescents with autism spectrum disorder and their fathers. NeuroImage, 49(1), 1055–
1065. https://doi.org/10.1016/j.neuroimage.2009.07.057
Griffin, C., Lombardo, M. V., & Auyeung, B. (2016). Alexithymia in children with and without
autism spectrum disorders. Autism Research, 9(7), 773–780.
https://doi.org/10.1002/aur.1569
Grynberg, D., Chang, B., Corneille, O., Maurage, P., Vermeulen, N., Berthoz, S., & Luminet, O.
(2012). Alexithymia and the Processing of Emotional Facial Expressions (EFEs):
Systematic Review, Unanswered Questions and Further Perspectives. PLOS ONE, 7(8),
e42429. https://doi.org/10.1371/journal.pone.0042429
Gunderson, J., Worthley, E., Byiers, B., Symons, F., & Wolff, J. (2023). Self and caregiver
report measurement of sensory features in autism spectrum disorder: A systematic review
of psychometric properties. Journal of Neurodevelopmental Disorders, 15(1), 5.
https://doi.org/10.1186/s11689-022-09473-7
Haidt, J. (2001). The emotional dog and its rational tail: A social intuitionist approach to moral
187
judgment. Psychological Review, 108(4), 814–834. https://doi.org/10.1037/0033-
295X.108.4.814
Haidt, J. (2012). The righteous mind: Why good people are divided by politics and religion (pp.
xvii, 421). Pantheon/Random House.
Haidt, J., McCauley, C., & Rozin, P. (1994). Individual differences in sensitivity to disgust: A
scale sampling seven domains of disgust elicitors. Personality and Individual
Differences, 16(5), 701–713. https://doi.org/10.1016/0191-8869(94)90212-7
Haidt, J., Rozin, P., Mccauley, C., & Imada, S. (1997). Body, Psyche, and Culture: The
Relationship between Disgust and Morality. Psychology and Developing Societies, 9(1),
107–131. https://doi.org/10.1177/097133369700900105
Hammell, K. R. W., & Iwama, M. K. (2012). Well-being and occupational rights: An imperative
for critical occupational therapy. Scandinavian Journal of Occupational Therapy, 19(5),
385–394. https://doi.org/10.3109/11038128.2011.611821
Harms, M. B., Martin, A., & Wallace, G. L. (2010). Facial Emotion Recognition in Autism
Spectrum Disorders: A Review of Behavioral and Neuroimaging Studies.
Neuropsychology Review, 20(3), 290–322. https://doi.org/10.1007/s11065-010-9138-6
Harrison, N. A., Gray, M. A., Gianaros, P. J., & Critchley, H. D. (2010). The Embodiment of
Emotional Feelings in the Brain. Journal of Neuroscience, 30(38), 12878–12884.
https://doi.org/10.1523/JNEUROSCI.1725-10.2010
Hart, N., McGowan, J., Minati, L., & Critchley, H. D. (2013). Emotional Regulation and Bodily
Sensation: Interoceptive Awareness Is Intact in Borderline Personality Disorder. Journal
of Personality Disorders, 27(4), 506–518. https://doi.org/10.1521/pedi_2012_26_049
Hasselkus, B. R. (2011). The Meaning of Everyday Occupation. SLACK Incorporated.
Haxby, J. V., Hoffman, E. A., & Gobbini, M. I. (2000). The distributed human neural system for
face perception. Trends in Cognitive Sciences, 4(6), 223–233.
https://doi.org/10.1016/S1364-6613(00)01482-0
Hedblom, C. (2019). Sense and Sensibility: Three Components of Moral Sensitivity and Their
Underlying Neural Mechanisms. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-17487
Hennenlotter, A., Schroeder, U., Erhard, P., Haslinger, B., Stahl, R., Weindl, A., von Einsiedel,
H. G., Lange, K. W., & Ceballos-Baumann, A. O. (2004). Neural correlates associated
with impaired disgust processing in pre-symptomatic Huntington’s disease. Brain: A
Journal of Neurology, 127(Pt 6), 1446–1453. https://doi.org/10.1093/brain/awh165
Hepper, P. G., Wells, D. L., Dornan, J. C., & Lynch, C. (2013). Long-term flavor recognition in
humans with prenatal garlic experience. Developmental Psychobiology, 55(5), 568–574.
https://doi.org/10.1002/dev.21059
188
Herz, R. (2013). That’s Disgusting: Unraveling the Mysteries of Repulsion (Illustrated edition).
W. W. Norton & Company.
Hill, A. P., Zuckerman, K. E., Hagen, A. D., Kriz, D. J., Duvall, S. W., van Santen, J., Nigg, J.,
Fair, D., & Fombonne, E. (2014). Aggressive behavior problems in children with autism
spectrum disorders: Prevalence and correlates in a large clinical sample. Research in
Autism Spectrum Disorders, 8(9), 1121–1133. https://doi.org/10.1016/j.rasd.2014.05.006
Hill, E. L. (2004). Executive dysfunction in autism. Trends in Cognitive Sciences, 8(1), 26–32.
https://doi.org/10.1016/j.tics.2003.11.003
Hirsch, C. R., Clark, D. M., & Mathews, A. (2006). Imagery and Interpretations in Social
Phobia: Support for the Combined Cognitive Biases Hypothesis. Behavior Therapy,
37(3), 223–236. https://doi.org/10.1016/j.beth.2006.02.001
Hirsch, C. R., Meeten, F., Krahé, C., & Reeder, C. (2016). Resolving Ambiguity in Emotional
Disorders: The Nature and Role of Interpretation Biases. Annual Review of Clinical
Psychology, 12(1), 281–305. https://doi.org/10.1146/annurev-clinpsy-021815-093436
Hitch, D., Pépin, G., & Stagnitti, K. (2014a). In the Footsteps of Wilcock, Part One: The
Evolution of Doing, Being, Becoming, and Belonging. Occupational Therapy In Health
Care, 28(3), 231–246. https://doi.org/10.3109/07380577.2014.898114
Hitch, D., Pépin, G., & Stagnitti, K. (2014b). In the Footsteps of Wilcock, Part Two: The
Interdependent Nature of Doing, Being, Becoming, and Belonging. Occupational
Therapy In Health Care, 28(3), 247–263. https://doi.org/10.3109/07380577.2014.898115
Hogeveen, J., & Grafman, J. (2021). Chapter 3—Alexithymia. In K. M. Heilman & S. E. Nadeau
(Eds.), Handbook of Clinical Neurology (Vol. 183, pp. 47–62). Elsevier.
https://doi.org/10.1016/B978-0-12-822290-4.00004-9
Holahan, L. F. (2014). Quality-in-Doing: Competence and Occupation. Journal of Occupational
Science, 21(4), 473–487. https://doi.org/10.1080/14427591.2013.815683
Holzer, P. (2017). Interoception and gut feelings: Unconscious body signals’ impact on brain
function, behavior and belief processes. In Processes of believing: The acquisition,
maintenance, and change in creditions (pp. 435–442). Springer International
Publishing/Springer Nature. https://doi.org/10.1007/978-3-319-50924-2_31
Horberg, E. J., Oveis, C., & Keltner, D. (2011). Emotions as Moral Amplifiers: An Appraisal
Tendency Approach to the Influences of Distinct Emotions upon Moral Judgment.
Emotion Review, 3(3), 237–244. https://doi.org/10.1177/1754073911402384
Horberg, E. J., Oveis, C., Keltner, D., & Cohen, A. B. (2009). Disgust and the moralization of
purity. Journal of Personality and Social Psychology, 97(6), 963–976.
https://doi.org/10.1037/a0017423
Houben, K., & Havermans, R. C. (2012). A delicious fly in the soup. The relationship between
189
disgust, obesity, and restraint. Appetite, 58(3), 827–830.
https://doi.org/10.1016/j.appet.2012.01.018
Howlin, P., Moss, P., Savage, S., & Rutter, M. (2013). Social Outcomes in Mid- to Later
Adulthood Among Individuals Diagnosed With Autism and Average Nonverbal IQ as
Children. Journal of the American Academy of Child & Adolescent Psychiatry, 52(6),
572-581.e1. https://doi.org/10.1016/j.jaac.2013.02.017
Hubl, D., Bölte, S., Feineis–Matthews, S., Lanfermann, H., Federspiel, A., Strik, W., Poustka, F.,
& Dierks, T. (2003). Functional imbalance of visual pathways indicates alternative face
processing strategies in autism. Neurology, 61(9), 1232.
https://doi.org/10.1212/01.WNL.0000091862.22033.1A
Hull, J. V., Dokovna, L. B., Jacokes, Z. J., Torgerson, C. M., Irimia, A., & Van Horn, J. D.
(2017). Resting-State Functional Connectivity in Autism Spectrum Disorders: A Review.
Frontiers in Psychiatry, 7. https://www.frontiersin.org/articles/10.3389/fpsyt.2016.00205
Huppert, F. A., Baylis, N., Keverne, B., & Davidson, R. J. (2004). Well–being and affective
style: Neural substrates and biobehavioural correlates. Philosophical Transactions of the
Royal Society of London. Series B: Biological Sciences, 359(1449), 1395–1411.
https://doi.org/10.1098/rstb.2004.1510
Ilioska, I., Oldehinkel, M., Llera, A., Chopra, S., Looden, T., Chauvin, R., Van Rooij, D., Floris,
D. L., Tillmann, J., Moessnang, C., Banaschewski, T., Holt, R. J., Loth, E., Charman, T.,
Murphy, D. G. M., Ecker, C., Mennes, M., Beckmann, C. F., Fornito, A., & Buitelaar, J.
K. (2023). Connectome-wide Mega-analysis Reveals Robust Patterns of Atypical
Functional Connectivity in Autism. Biological Psychiatry, 94(1), 29–39.
https://doi.org/10.1016/j.biopsych.2022.12.018
Ille, R., Wolf, A., Tomazic, P. V., & Schienle, A. (2017). Hyposmia and Disgust: GenderSpecific Effects. Chemical Senses, 42(6), 493–497.
https://doi.org/10.1093/chemse/bjw111
Inbar, Y., & Pizarro, D. A. (2022). Chapter Three—How disgust affects social judgments. In B.
Gawronski (Ed.), Advances in Experimental Social Psychology (Vol. 65, pp. 109–166).
Academic Press. https://doi.org/10.1016/bs.aesp.2021.11.002
Inbar, Y., Pizarro, D. A., Knobe, J., & Bloom, P. (2009). Disgust sensitivity predicts intuitive
disapproval of gays. Emotion, 9(3), 435–439. https://doi.org/10.1037/a0015960
Inbar, Y., Pizarro, D., Iyer, R., & Haidt, J. (2012). Disgust Sensitivity, Political Conservatism,
and Voting. Social Psychological and Personality Science, 3(5), 537–544.
https://doi.org/10.1177/1948550611429024
Iyer, R., Koleva, S., Graham, J., Ditto, P., & Haidt, J. (2012). Understanding Libertarian
Morality: The Psychological Dispositions of Self-Identified Libertarians. PLOS ONE,
7(8), e42366. https://doi.org/10.1371/journal.pone.0042366
190
Izard, C. E. (1993). Organizational and motivational functions of discrete emotions. In
Handbook of emotions (pp. 631–641). The Guilford Press.
Izard, C. E. (2011). Forms and functions of emotions: Matters of emotion–cognition interactions.
Emotion Review, 3, 371–378. https://doi.org/10.1177/1754073911410737
Jaarsma, P., & Welin, S. (2012). Autism as a natural human variation: Reflections on the claims
of the neurodiversity movement. Health Care Analysis, 20(1), 20–30.
https://doi.org/10.1007/s10728-011-0169-9
Jabbi, M., Bastiaansen, J., & Keysers, C. (2008). A Common Anterior Insula Representation of
Disgust Observation, Experience and Imagination Shows Divergent Functional
Connectivity Pathways. PLOS ONE, 3(8), e2939.
https://doi.org/10.1371/journal.pone.0002939
Jackson, J., Carlson, M., Mandel, D., Zemke, R., & Clark, F. (1998). Occupation in Lifestyle
Redesign: The Well Elderly Study Occupational Therapy Program. American Journal of
Occupational Therapy, 52(5), 326–336. https://doi.org/10.5014/ajot.52.5.326
Jager, I., Koning, P. de, Bost, T., Denys, D., & Vulink, N. (2020). Misophonia: Phenomenology,
comorbidity and demographics in a large sample. PLOS ONE, 15(4), e0231390.
https://doi.org/10.1371/journal.pone.0231390
Janouschek, H., Chase, H. W., Sharkey, R. J., Peterson, Z. J., Camilleri, J. A., Abel, T., Eickhoff,
S. B., & Nickl-Jockschat, T. (2021). The functional neural architecture of dysfunctional
reward processing in autism. NeuroImage: Clinical, 31, 102700.
https://doi.org/10.1016/j.nicl.2021.102700
Jarvis Thomson, J. (1985). The trolley problem. Yale Law Journal, 94(6), 5.
Jayashankar, A., & Aziz-Zadeh, L. (2023). Disgust Processing and Potential Relationships with
Behaviors in Autism. Current Psychiatry Reports, 25(10), 465–478.
https://doi.org/10.1007/s11920-023-01445-5
Jayashankar, A., Aziz-Zadeh, L., & Ringold, S. (2022). Morality and disgust in children with
Autism Spectrum Disorder (ASD). https://doi.org/10.17605/OSF.IO/UWSD3
Jayashankar, A., Bynum, B., Butera, C., Kilroy, E., Harrison, L., & Aziz-Zadeh, L. (2023).
Connectivity differences between inferior frontal gyrus and mentalizing network in
autism as compared to developmental coordination disorder and non-autistic youth.
Cortex. https://doi.org/10.1016/j.cortex.2023.06.014
Jayashankar, A., Kilroy, E., Butera, C., Harrison, L., Ringold, S., & Aziz-Zadeh, L. (2022).
Anterior insula reactivity is associated with disgust propensity in children with Autism
Spectrum disorder (ASD). ISRE 2022 Program, 168.
https://doi.org/10.13140/RG.2.2.25569.89444
Jayashankar, A., Kilroy, E., Ringold, S., Butera, C., McGuire, R., & Aziz-Zadeh, L. (under
191
review). Disgust processing differences and their neural correlates in autistic youth.
Scientific Reports.
Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002). Improved optimization for the
robust and accurate linear registration and motion correction of brain images.
NeuroImage, 17(2), 825–841. https://doi.org/10.1016/s1053-8119(02)91132-8
Jenkinson, M., Beckmann, C. F., Behrens, T. E. J., Woolrich, M. W., & Smith, S. M. (2012).
FSL. NeuroImage, 62(2), 782–790. https://doi.org/10.1016/j.neuroimage.2011.09.015
Jenkinson, M., & Smith, S. (2001). A global optimisation method for robust affine registration of
brain images. Medical Image Analysis, 5(2), 143–156. https://doi.org/10.1016/s1361-
8415(01)00036-6
Kalyva, E. (2009). Comparison of Eating Attitudes between Adolescent Girls with and without
Asperger Syndrome: Daughters’ and Mothers’ Reports. Journal of Autism and
Developmental Disorders, 39(3), 480–486. https://doi.org/10.1007/s10803-008-0648-5
Kalyva, E., Pellizzoni, S., Tavano, A., Iannello, P., & Siegal, M. (2010). Contamination
sensitivity in autism, Down syndrome, and typical development. Research in Autism
Spectrum Disorders, 4(1), 43–50. https://doi.org/10.1016/j.rasd.2009.07.005
Kangarani-Farahani, M., Izadi-Najafabadi, S., & Zwicker, J. G. (2022). How does brain structure
and function on MRI differ in children with autism spectrum disorder, developmental
coordination disorder, and/or attention deficit hyperactivity disorder? International
Journal of Developmental Neuroscience, 82(8), 680–714.
https://doi.org/10.1002/jdn.10228
Kasari, C., Locke, J., Gulsrud, A., & Rotheram-Fuller, E. (2011). Social Networks and
Friendships at School: Comparing Children With and Without ASD. Journal of Autism
and Developmental Disorders, 41(5), 533–544. https://doi.org/10.1007/s10803-010-
1076-x
Katarzyna, C., Fred, V., & Ami, K. (2010). Limited Attentional Bias for Faces in Toddlers With
Autism Spectrum Disorders. Archives of General Psychiatry, 67(2), 178.
https://doi.org/10.1001/archgenpsychiatry.2009.194
Keysers, C., & Gazzola, V. (2009). Expanding the mirror: Vicarious activity for actions,
emotions, and sensations. Current Opinion in Neurobiology, 19(6), 666–671.
https://doi.org/10.1016/j.conb.2009.10.006
Kilroy, E., Harrison, L., Butera, C., Jayashankar, A., Cermak, S., Kaplan, J., Williams, M.,
Haranin, E., Bookheimer, S., Dapretto, M., & Aziz‐Zadeh, L. (2021). Unique deficit in
embodied simulation in autism: An fMRI study comparing autism and developmental
coordination disorder. Human Brain Mapping, 42(5), 1532–1546.
https://doi.org/10.1002/hbm.25312
King-Casas, B., Sharp, C., Lomax-Bream, L., Lohrenz, T., Fonagy, P., & Montague, P. R.
192
(2008). The rupture and repair of cooperation in borderline personality disorder. Science
(New York, N.Y.), 321(5890), 806–810. https://doi.org/10.1126/science.1156902
Kirby, A. V., Bilder, D. A., Wiggins, L. D., Hughes, M. M., Davis, J., Hall-Lande, J. A., Lee, L.-
C., McMahon, W. M., & Bakian, A. V. (2022). Sensory features in autism: Findings from
a large population-based surveillance system. Autism Research, 15(4), 751–760.
https://doi.org/10.1002/aur.2670
Kirby, L. A. J., & Robinson, J. L. (2017). Affective mapping: An activation likelihood
estimation (ALE) meta-analysis. Brain and Cognition, 118, 137–148.
https://doi.org/10.1016/j.bandc.2015.04.006
Kleinhans, N. M., Johnson, L. C., Richards, T., Mahurin, R., Greenson, J., Dawson, G., &
Aylward, E. (2009). Reduced neural habituation in the amygdala and social impairments
in autism spectrum disorders. The American Journal of Psychiatry, 166(4), 467–475.
https://doi.org/10.1176/appi.ajp.2008.07101681
Kleinknecht, R. A., Kleinknecht, E. E., & Thorndike, R. M. (1997). The role of disgust and fear
in blood and injection—Related fainting symptoms: A structural equation model.
Behaviour Research and Therapy, 35(12), 1075–1087. https://doi.org/10.1016/S0005-
7967(97)80002-2
Knowles, K. A., Cox, R. C., Armstrong, T., & Olatunji, B. O. (2019). Cognitive mechanisms of
disgust in the development and maintenance of psychopathology: A qualitative review
and synthesis. Clinical Psychology Review, 69, 30–50.
https://doi.org/10.1016/j.cpr.2018.06.002
Knowles, K. A., Jessup, S. C., & Olatunji, B. O. (2018). Disgust in Anxiety and ObsessiveCompulsive Disorders: Recent Findings and Future Directions. Current Psychiatry
Reports, 20(9), 68. https://doi.org/10.1007/s11920-018-0936-5
Kohlberg, L. (1969). Stage and sequence: The cognitive-developmental approach to
socialization. Handbook of Socialization Theory and Research, 347, 480.
Kohlberg, L. (1971). From is to out: How to commit the naturalistic fallacy and get away with it
in the study of moral development. Cognitive Development and Epistemology.
Konishi, N., Himichi, T., & Ohtsubo, Y. (2020). Heart rate reveals the difference between
disgust and anger in the domain of morality. Evolutionary Behavioral Sciences, 14(3),
284–298. https://doi.org/10.1037/ebs0000179
Koob, G. F., Arends, M. A., McCracken, M., & Le Moal, M. (2019). Chapter 3—
Neurobiological theories of addiction. In G. F. Koob, M. A. Arends, M. McCracken, &
M. Le Moal (Eds.), Introduction to Addiction (Vol. 1, pp. 125–262). Academic Press.
https://doi.org/10.1016/B978-0-12-816863-9.00003-0
Korkman, M., Kirk, U., & Kemp, S. (2007). NEPSY - Second Edition.
https://doi.org/10.1037/t15125-000
193
Koster-Hale, J., Dungan, J., Saxe, R., & Young, L. (2012). Thinking in Patterns: Using multivoxel pattern analyses to find neural correlates of moral judgment in neurotypical and
ASD populations. Proceedings of the Annual Meeting of the Cognitive Science Society,
34(34). https://escholarship.org/uc/item/0km5x647
Kral, T. V. E., Eriksen, W. T., Souders, M. C., & Pinto-Martin, J. A. (2013). Eating Behaviors,
Diet Quality, and Gastrointestinal Symptoms in Children With Autism Spectrum
Disorders: A Brief Review. Journal of Pediatric Nursing, 28(6), 548–556.
https://doi.org/10.1016/j.pedn.2013.01.008
Kumazaki, H., Muramatsu, T., Fujisawa, T. X., Miyao, M., Matsuura, E., Okada, K., Kosaka, H.,
Tomoda, A., & Mimura, M. (2016). Assessment of olfactory detection thresholds in
children with autism spectrum disorders using a pulse ejection system. Molecular Autism,
7(1), 6. https://doi.org/10.1186/s13229-016-0071-2
Kumazaki, H., Muramatsu, T., Miyao, M., Okada, K., Mimura, M., & Kikuchi, M. (2019). Brief
Report: Olfactory Adaptation in Children with Autism Spectrum Disorders. Journal of
Autism and Developmental Disorders, 49(8), 3462–3469. https://doi.org/10.1007/s10803-
019-04053-6
Kupfer, T. R. (2018). Why are injuries disgusting? Comparing pathogen avoidance and empathy
accounts. Emotion, 18(7), 959–970. https://doi.org/10.1037/emo0000395
Lakoff, G., & Johnson, M. (2008). Metaphors We Live By. University of Chicago Press.
Lamm, C., & Singer, T. (2010). The role of anterior insular cortex in social emotions. Brain
Structure and Function, 214(5), 579–591. https://doi.org/10.1007/s00429-010-0251-3
Lassalle, A., Zürcher, N. R., Porro, C. A., Benuzzi, F., Hippolyte, L., Lemonnier, E., Åsberg
Johnels, J., & Hadjikhani, N. (2019). Influence of anxiety and alexithymia on brain
activations associated with the perception of others’ pain in autism. Social Neuroscience,
14(3), 359–377. https://doi.org/10.1080/17470919.2018.1468358
Law Smith, M. J., Montagne, B., Perrett, D. I., Gill, M., & Gallagher, L. (2010). Detecting subtle
facial emotion recognition deficits in high-functioning Autism using dynamic stimuli of
varying intensities. Neuropsychologia, 48(9), 2777–2781.
https://doi.org/10.1016/j.neuropsychologia.2010.03.008
Lawson, C., MacLeod, C., & Hammond, G. (2002). Interpretation revealed in the blink of an
eye: Depressive bias in the resolution of ambiguity. Journal of Abnormal Psychology,
111(2), 321–328. https://doi.org/10.1037/0021-843X.111.2.321
Leathers-Smith, E., & Davey, G. C. L. (2011). The Disgust Threat Interpretation Bias is Not
Moderated by Anxiety & Disgust Sensitivity. Journal of Experimental Psychopathology,
2(1), 63–76. https://doi.org/10.5127/jep.007410
Lee, D. S., Kim, E., & Schwarz, N. (2015). Something smells fishy: Olfactory suspicion cues
improve performance on the Moses illusion and Wason rule discovery task. Journal of
194
Experimental Social Psychology, 59, 47–50. https://doi.org/10.1016/j.jesp.2015.03.006
Lee, J. M., Kyeong, S., Kim, E., & Cheon, K.-A. (2016). Abnormalities of Inter- and IntraHemispheric Functional Connectivity in Autism Spectrum Disorders: A Study Using the
Autism Brain Imaging Data Exchange Database. Frontiers in Neuroscience, 10.
https://www.frontiersin.org/articles/10.3389/fnins.2016.00191
Lee, S. W. S., & Ellsworth, P. C. (2013). Maggots and morals: Physical disgust is to fear as
moral disgust is to anger. In Components of emotional meaning: A sourcebook (pp. 271–
280). Oxford University Press.
https://doi.org/10.1093/acprof:oso/9780199592746.003.0018
Lee, S. W. S., & Schwarz, N. (2012). Bidirectionality, mediation, and moderation of
metaphorical effects: The embodiment of social suspicion and fishy smells. Journal of
Personality and Social Psychology, 103(5), 737–749. https://doi.org/10.1037/a0029708
Leufstadius, C., Erlandsson, L.-K., Björkman, T., & Eklund, M. (2008). Meaningfulness in daily
occupations among individuals with persistent mental illness. Journal of Occupational
Science, 15(1), 27–35. https://doi.org/10.1080/14427591.2008.9686604
Leung, R. C., Ye, A. X., Wong, S. M., Taylor, M. J., & Doesburg, S. M. (2014). Reduced beta
connectivity during emotional face processing in adolescents with autism. Molecular
Autism, 5, 51. https://doi.org/10.1186/2040-2392-5-51
Levenson, R. W. (1992). Autonomic Nervous System Differences among Emotions.
Psychological Science, 3(1), 23–27. https://doi.org/10.1111/j.1467-9280.1992.tb00251.x
Levenson, R. W. (2011). Basic emotion questions. Emotion Review, 3, 379–386.
https://doi.org/10.1177/1754073911410743
Levenson, R. W., Ekman, P., & Friesen, W. V. (1990). Voluntary Facial Action Generates
Emotion-Specific Autonomic Nervous System Activity. Psychophysiology, 27(4), 363–
384. https://doi.org/10.1111/j.1469-8986.1990.tb02330.x
Li, J., Zhu, L., & Gummerum, M. (2014). The relationship between moral judgment and
cooperation in children with high-functioning autism. Scientific Reports, 4(1), Article 1.
https://doi.org/10.1038/srep04314
Lim, J., Kurnianingsih, Y. A., Ong, H. H., & Mullette-Gillman, O. A. (2017). Moral judgment
modulation by disgust priming via altered fronto-temporal functional connectivity.
Scientific Reports, 7(1), Article 1. https://doi.org/10.1038/s41598-017-11147-7
Liu, T.-L., Wang, P.-W., Yang, Y.-H. C., Shyi, G. C.-W., & Yen, C.-F. (2019). Association
between Facial Emotion Recognition and Bullying Involvement among Adolescents with
High-Functioning Autism Spectrum Disorder. International Journal of Environmental
Research and Public Health, 16(24), 5125. https://doi.org/10.3390/ijerph16245125
Liuzza, M. T., Olofsson, J. K., Cancino-Montecinos, S., & Lindholm, T. (2019). Body Odor
195
Disgust Sensitivity Predicts Moral Harshness Toward Moral Violations of Purity.
Frontiers in Psychology, 10.
https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2019.00458
Loas, G., Braun, S., Delhaye, M., & Linkowski, P. (2017). The measurement of alexithymia in
children and adolescents: Psychometric properties of the Alexithymia Questionnaire for
Children and the twenty-item Toronto Alexithymia Scale in different non-clinical and
clinical samples of children and adolescents. PloS One, 12(5), e0177982.
https://doi.org/10.1371/journal.pone.0177982
Lord, C. E. (2010). Autism: From research to practice. American Psychologist, 65(8), 815–826.
https://doi.org/10.1037/0003-066X.65.8.815
Lord, C., Rutter, M., DiLavore, P., Risi, S., Gotham, K., & Bishop, S. (2012). Autism diagnostic
observation schedule–2nd edition (ADOS-2). Los Angeles, CA: Western Psychological
Corporation, 284.
Lord, C., Rutter, M., & Le Couteur, A. (1994). Autism Diagnostic Interview-Revised: A revised
version of a diagnostic interview for caregivers of individuals with possible pervasive
developmental disorders. Journal of Autism and Developmental Disorders, 24(5), 659–
685.
Losh, M., & Capps, L. (2006). Understanding of emotional experience in autism: Insights from
the personal accounts of high-functioning children with autism. Developmental
Psychology, 42(5), 809–818. https://doi.org/10.1037/0012-1649.42.5.809
Luisier, A.-C., Petitpierre, G., Ferdenzi, C., Clerc Bérod, A., Giboreau, A., Rouby, C., &
Bensafi, M. (2015). Odor Perception in Children with Autism Spectrum Disorder and its
Relationship to Food Neophobia. Frontiers in Psychology, 6.
https://www.frontiersin.org/articles/10.3389/fpsyg.2015.01830
Madra, M., Ringel, R., & Margolis, K. G. (2020). Gastrointestinal Issues and Autism Spectrum
Disorder. Child and Adolescent Psychiatric Clinics of North America, 29(3), 501–513.
https://doi.org/10.1016/j.chc.2020.02.005
Marco, E. J., Hinkley, L. B. N., Hill, S. S., & Nagarajan, S. S. (2011). Sensory Processing in
Autism: A Review of Neurophysiologic Findings. Pediatric Research, 69(8), Article 8.
https://doi.org/10.1203/PDR.0b013e3182130c54
Marek, S., Tervo-Clemmens, B., Calabro, F. J., Montez, D. F., Kay, B. P., Hatoum, A. S.,
Donohue, M. R., Foran, W., Miller, R. L., Hendrickson, T. J., Malone, S. M., Kandala,
S., Feczko, E., Miranda-Dominguez, O., Graham, A. M., Earl, E. A., Perrone, A. J.,
Cordova, M., Doyle, O., … Dosenbach, N. U. F. (2022). Reproducible brain-wide
association studies require thousands of individuals. Nature, 603(7902), Article 7902.
https://doi.org/10.1038/s41586-022-04492-9
Margoni, F., Guglielmetti, G., & Surian, L. (2019). Brief Report: Young Children with Autism
Can Generate Intent-Based Moral Judgments. Journal of Autism and Developmental
196
Disorders, 49(12), 5078–5085. https://doi.org/10.1007/s10803-019-04212-9
Margoni, F., & Surian, L. (2016). Mental State Understanding and Moral Judgment in Children
with Autistic Spectrum Disorder. Frontiers in Psychology, 7.
https://doi.org/10.3389/fpsyg.2016.01478
Martins, Y., & Pliner, P. (2006). “Ugh! That’s disgusting!”: Identification of the characteristics
of foods underlying rejections based on disgust. Appetite, 46(1), 75–85.
https://doi.org/10.1016/j.appet.2005.09.001
Marzillier, S., & Davey, G. (2004). The emotional profiling of disgust‐eliciting stimuli: Evidence
for primary and complex disgusts. Cognition and Emotion, 18(3), 313–336.
https://doi.org/10.1080/02699930341000130
Masi, A., DeMayo, M. M., Glozier, N., & Guastella, A. J. (2017). An Overview of Autism
Spectrum Disorder, Heterogeneity and Treatment Options. Neuroscience Bulletin, 33(2),
183–193. https://doi.org/10.1007/s12264-017-0100-y
Matheson, B. E., & Douglas, J. M. (2017). Overweight and Obesity in Children with Autism
Spectrum Disorder (ASD): A Critical Review Investigating the Etiology, Development,
and Maintenance of this Relationship. Review Journal of Autism and Developmental
Disorders, 4(2), 142–156. https://doi.org/10.1007/s40489-017-0103-7
Matson, J. L., Fodstad, J. C., & Dempsey, T. (2009). The relationship of children’s feeding
problems to core symptoms of autism and PDD-NOS. Research in Autism Spectrum
Disorders, 3(3), 759–766. https://doi.org/10.1016/j.rasd.2009.02.005
Mayer, B., Muris, P., Busser, K., & Bergamin, J. (2009). A disgust mood state causes a negative
interpretation bias, but not in the specific domain of body-related concerns. Behaviour
Research and Therapy, 47(10), 876–881. https://doi.org/10.1016/j.brat.2009.07.001
Mayer, E. A., Knight, R., Mazmanian, S. K., Cryan, J. F., & Tillisch, K. (2014). Gut Microbes
and the Brain: Paradigm Shift in Neuroscience. Journal of Neuroscience, 34(46), 15490–
15496. https://doi.org/10.1523/JNEUROSCI.3299-14.2014
Mayes, S. D., Calhoun, S. L., Aggarwal, R., Baker, C., Mathapati, S., Anderson, R., & Petersen,
C. (2012). Explosive, oppositional, and aggressive behavior in children with autism
compared to other clinical disorders and typical children. Research in Autism Spectrum
Disorders, 6(1), 1–10. https://doi.org/10.1016/j.rasd.2011.08.001
Mayes, S. D., & Zickgraf, H. (2019). Atypical eating behaviors in children and adolescents with
autism, ADHD, other disorders, and typical development. Research in Autism Spectrum
Disorders, 64, 76–83. https://doi.org/10.1016/j.rasd.2019.04.002
Mazza, M., Pino, M. C., Mariano, M., Tempesta, D., Ferrara, M., De Berardis, D., Masedu, F., &
Valenti, M. (2014). Affective and cognitive empathy in adolescents with autism spectrum
disorder. Frontiers in Human Neuroscience, 8.
https://doi.org/10.3389/fnhum.2014.00791
197
Mazzoni, D., Cicognani, E., Tzankova, I., Guarino, A., Albanesi, C., & Zani, B. (2020). Civic
Participation and Other Interventions That Promote Children’s Tolerance of Migrants. In
N. Balvin & D. J. Christie (Eds.), Children and Peace: From Research to Action (pp. 89–
102). Springer International Publishing. https://doi.org/10.1007/978-3-030-22176-8_6
McKay, E. A. (2020). The dark side of occupation: A historical review of occupational therapy.
In Illuminating The Dark Side of Occupation (pp. 26–34). Routledge.
McLaren, D. G., Ries, M. L., Xu, G., & Johnson, S. C. (2012). A generalized form of contextdependent psychophysiological interactions (gPPI): A comparison to standard
approaches. NeuroImage, 61(4), 1277–1286.
https://doi.org/10.1016/j.neuroimage.2012.03.068
McNally, R. J. (2002). Disgust has arrived. The Role of Disgust in Anxiety Disorders, 16(5),
561–566. https://doi.org/10.1016/S0887-6185(02)00174-3
Mendez, M. F. (2023). A Functional and Neuroanatomical Model of Dehumanization. Cognitive
and Behavioral Neurology, 36(1), 42. https://doi.org/10.1097/WNN.0000000000000316
Mennella, J. A., Jagnow, C. P., & Beauchamp, G. K. (2001). Prenatal and Postnatal Flavor
Learning by Human Infants. Pediatrics, 107(6), e88.
https://doi.org/10.1542/peds.107.6.e88
Menon, V., & Uddin, L. Q. (2010). Saliency, switching, attention and control: A network model
of insula function. Brain Structure & Function, 214(5–6), 655–667.
https://doi.org/10.1007/s00429-010-0262-0
Miller Jr, H. L. (2016). The SAGE Encyclopedia of Theory in Psychology. SAGE.
Miller, W. I. (1998). The Anatomy of Disgust. Harvard University Press.
Milosavljevic, B., Carter Leno, V., Simonoff, E., Baird, G., Pickles, A., Jones, C. R. G., Erskine,
C., Charman, T., & Happé, F. (2016). Alexithymia in Adolescents with Autism Spectrum
Disorder: Its Relationship to Internalising Difficulties, Sensory Modulation and Social
Cognition. Journal of Autism and Developmental Disorders, 46(4), 1354–1367.
https://doi.org/10.1007/s10803-015-2670-8
Moll, J., de Oliveira-Souza, R., Moll, F. T., Ignácio, F. A., Bramati, I. E., Caparelli-Dáquer, E.
M., & Eslinger, P. J. (2005). The Moral Affiliations of Disgust: A Functional MRI Study.
Cognitive and Behavioral Neurology, 18(1), 68–78.
https://doi.org/10.1097/01.wnn.0000152236.46475.a7
Molnar-Szakacs, I., & Uddin, L. Q. (2022). Anterior insula as a gatekeeper of executive control.
Neuroscience & Biobehavioral Reviews, 139, 104736.
https://doi.org/10.1016/j.neubiorev.2022.104736
Monk, C. S., Weng, S.-J., Wiggins, J. L., Kurapati, N., Louro, H. M. C., Carrasco, M.,
Maslowsky, J., Risi, S., & Lord, C. (2010). Neural circuitry of emotional face processing
198
in autism spectrum disorders. Journal of Psychiatry & Neuroscience : JPN, 35(2), 105–
114. https://doi.org/10.1503/jpn.090085
Moretto, G., Làdavas, E., Mattioli, F., & di Pellegrino, G. (2010). A Psychophysiological
Investigation of Moral Judgment after Ventromedial Prefrontal Damage. Journal of
Cognitive Neuroscience, 22(8), 1888–1899. https://doi.org/10.1162/jocn.2009.21367
Mul, C., Stagg, S. D., Herbelin, B., & Aspell, J. E. (2018). The Feeling of Me Feeling for You:
Interoception, Alexithymia and Empathy in Autism. Journal of Autism and
Developmental Disorders, 48(9), 2953–2967. https://doi.org/10.1007/s10803-018-3564-3
Muratori, F., Tonacci, A., Billeci, L., Catalucci, T., Igliozzi, R., Calderoni, S., & Narzisi, A.
(2017). Olfactory Processing in Male Children with Autism: Atypical Odor Threshold
and Identification. Journal of Autism and Developmental Disorders, 47(10), 3243–3251.
https://doi.org/10.1007/s10803-017-3250-x
Muris, P., & Field, A. P. (2008). Distorted cognition and pathological anxiety in children and
adolescents. Cognition and Emotion, 22(3), 395–421.
https://doi.org/10.1080/02699930701843450
Muris, P., Huijding, J., Mayer, B., Langkamp, M., Reyhan, E., & Olatunji, B. (2012).
Assessment of Disgust Sensitivity in Children With an Age-Downward Version of the
Disgust Emotion Scale. Special Series: Overcoming the Glass Ceiling, 43(4), 876–886.
https://doi.org/10.1016/j.beth.2012.03.002
Muris, P., van der Heiden, S., & Rassin, E. (2008). Disgust sensitivity and psychopathological
symptoms in non-clinical children. Journal of Behavior Therapy and Experimental
Psychiatry, 39(2), 133–146. https://doi.org/10.1016/j.jbtep.2007.02.001
Murphy, F. C., Nimmo-Smith, I., & Lawrence, A. D. (2003). Functional neuroanatomy of
emotions: A meta-analysis. Cognitive, Affective, & Behavioral Neuroscience, 3(3), 207–
233. https://doi.org/10.3758/CABN.3.3.207
Nadeau, M. V., Richard, E., & Wallace, G. L. (2022). The Combination of Food Approach and
Food Avoidant Behaviors in Children with Autism Spectrum Disorder: “Selective
Overeating.” Journal of Autism and Developmental Disorders, 52(3), 987–994.
https://doi.org/10.1007/s10803-021-04945-6
Nelson, D. L. (1997). Why the Profession of Occupational Therapy Will Flourish in the 21st
Century. American Journal of Occupational Therapy, 51(1), 11–24.
https://doi.org/10.5014/ajot.51.1.11
Nelson, S. M., Dosenbach, N. U. F., Cohen, A. L., Wheeler, M. E., Schlaggar, B. L., & Petersen,
S. E. (2010). Role of the anterior insula in task-level control and focal attention. Brain
Structure and Function, 214(5), 669–680. https://doi.org/10.1007/s00429-010-0260-2
Nestor, P. J., Graham, N. L., Fryer, T. D., Williams, G. B., Patterson, K., & Hodges, J. R. (2003).
Progressive non‐fluent aphasia is associated with hypometabolism centred on the left
199
anterior insula. Brain, 126(11), 2406–2418. https://doi.org/10.1093/brain/awg240
Nichols, S. (2002). Norms with feeling: Towards a psychological account of moral judgment.
Cognition, 84(2), 221–236. https://doi.org/10.1016/S0010-0277(02)00048-3
Nickl-Jockschat, T., Habel, U., Maria Michel, T., Manning, J., Laird, A. R., Fox, P. T.,
Schneider, F., & Eickhoff, S. B. (2012). Brain structure anomalies in autism spectrum
disorder—A meta-analysis of VBM studies using anatomic likelihood estimation. Human
Brain Mapping, 33(6), 1470–1489. https://doi.org/10.1002/hbm.21299
Noel, J.-P., Lytle, M., Cascio, C., & Wallace, M. T. (2018). Disrupted integration of
exteroceptive and interoceptive signaling in autism spectrum disorder. Autism Research,
11(1), 194–205. https://doi.org/10.1002/aur.1880
Nomi, J. S., Molnar-Szakacs, I., & Uddin, L. Q. (2019). Insular function in autism: Update and
future directions in neuroimaging and interventions. Progress in NeuroPsychopharmacology and Biological Psychiatry, 89, 412–426.
https://doi.org/10.1016/j.pnpbp.2018.10.015
Nomi, J. S., & Uddin, L. Q. (2015a). Developmental changes in large-scale network connectivity
in autism. NeuroImage. Clinical, 7, 732–741. https://doi.org/10.1016/j.nicl.2015.02.024
Nomi, J. S., & Uddin, L. Q. (2015b). Face processing in autism spectrum disorders: From brain
regions to brain networks. Neuropsychologia, 71, 201–216.
https://doi.org/10.1016/j.neuropsychologia.2015.03.029
Oakley, B. F. M., Brewer, R., Bird, G., & Catmur, C. (2016). Theory of Mind Is Not Theory of
Emotion: A Cautionary Note on the Reading the Mind in the Eyes Test. Journal of
Abnormal Psychology, 125(6), 818–823. https://doi.org/10.1037/abn0000182
Oaten, M., Stevenson, R. J., & Case, T. I. (2009). Disgust as a disease-avoidance mechanism.
Psychological Bulletin, 135(2), 303–321. https://doi.org/10.1037/a0014823
Oaten, M., Stevenson, R. J., Wagland, P., Case, T. I., & Repacholi, B. M. (2014). Parent-Child
Transmission of Disgust and Hand Hygiene: The Role of Vocalizations, Gestures and
Other Parental Responses. The Psychological Record, 64(4), 803–811. Health Research
Premium Collection; ProQuest Central; SciTech Premium Collection; Social Science
Premium Collection. https://doi.org/10.1007/s40732-014-0044-9
Ogar, J., Willock, S., Baldo, J., Wilkins, D., Ludy, C., & Dronkers, N. (2006). Clinical and
anatomical correlates of apraxia of speech. Brain and Language, 97(3), 343–350.
https://doi.org/10.1016/j.bandl.2006.01.008
Ola, L., & Gullon-Scott, F. (2020). Facial emotion recognition in autistic adult females correlates
with alexithymia, not autism. Autism, 24(8), 2021–2034.
https://doi.org/10.1177/1362361320932727
Olatunji, B. O., Adams, T., Ciesielski, B., David, B., Sarawgi, S., & Broman-Fulks, J. (2012).
200
The Three Domains of Disgust Scale: Factor structure, psychometric properties, and
conceptual limitations. Assessment, 19(2), 205–225.
https://doi.org/10.1177/1073191111432881
Olatunji, B. O., Armstrong, T., & Elwood, L. (2017). Is Disgust Proneness Associated With
Anxiety and Related Disorders? A Qualitative Review and Meta-Analysis of Group
Comparison and Correlational Studies. Perspectives on Psychological Science, 12(4),
613–648. https://doi.org/10.1177/1745691616688879
Olatunji, B. O., & Cisler, J. M. (2009). Disgust sensitivity: Psychometric overview and
operational definition. In Disgust and its disorders: Theory, assessment, and treatment
implications (pp. 31–56). American Psychological Association.
https://doi.org/10.1037/11856-002
Olatunji, B. O., Cisler, J. M., Deacon, B. J., Connolly, K., & Lohr, J. M. (2007). The Disgust
Propensity and Sensitivity Scale-Revised: Psychometric properties and specificity in
relation to anxiety disorder symptoms. Journal of Anxiety Disorders, 21(7), 918–930.
https://doi.org/10.1016/j.janxdis.2006.12.005
Olatunji, B. O., Cisler, J., McKay, D., & Phillips, M. L. (2010). Is disgust associated with
psychopathology? Emerging research in the anxiety disorders. Psychiatry Research,
175(1), 1–10. https://doi.org/10.1016/j.psychres.2009.04.007
Olatunji, B. O., Ebesutani, C., & Kim, E. H. (2016). Does the measure matter? On the
association between disgust proneness and OCD symptoms. Journal of Anxiety
Disorders, 44, 63–72. https://doi.org/10.1016/j.janxdis.2016.10.010
Olatunji, B. O., Haidt, J., McKay, D., & David, B. (2008). Core, animal reminder, and
contamination disgust: Three kinds of disgust with distinct personality, behavioral,
physiological, and clinical correlates. Journal of Research in Personality, 42(5), 1243–
1259. https://doi.org/10.1016/j.jrp.2008.03.009
Olatunji, B. O., & McKay, D. (2007). Disgust and psychiatric illness: Have we remembered?
The British Journal of Psychiatry, 190, 457–459.
https://doi.org/10.1192/bjp.bp.106.032631
Olatunji, B. O., Puncochar, B. D., & Cox, R. (2016). Effects of Experienced Disgust on MorallyRelevant Judgments. PLOS ONE, 11(8), e0160357.
https://doi.org/10.1371/journal.pone.0160357
Olatunji, B. O., & Sawchuk, C. N. (2005). Disgust: Characteristic Features, Social
Manifestations, and Clinical Implications. Journal of Social and Clinical Psychology,
24(7), 932–962. https://doi.org/10.1521/jscp.2005.24.7.932
Oldfield, R. C. (1971). The assessment and analysis of handedness: The Edinburgh inventory.
Neuropsychologia, 9(1), 97–113. https://doi.org/10.1016/0028-3932(71)90067-4
Orwell, G. (1958). The road to Wigan Pier. Harcourt, Brace.
201
Ottaviani, C., Mancini, F., Petrocchi, N., Medea, B., & Couyoumdjian, A. (2013). Autonomic
correlates of physical and moral disgust. International Journal of Psychophysiology,
89(1), 57–62. https://doi.org/10.1016/j.ijpsycho.2013.05.003
Overton, P. G., Vivas, A. B., & Simpson, J. (2021). Disgust in Neurological Disorders. In P. A.
Powell & N. S. Consedine (Eds.), The Handbook of Disgust Research: Modern
Perspectives and Applications (pp. 209–223). Springer International Publishing.
https://doi.org/10.1007/978-3-030-84486-8_12
Ozonoff, S., Pennington, B. F., & Rogers, S. J. (1990). Are there emotion perception deficits in
young autistic children? Journal of Child Psychology and Psychiatry, and Allied
Disciplines, 31(3), 343–361. https://doi.org/10.1111/j.1469-7610.1990.tb01574.x
Pace-Schott, E. F., Amole, M. C., Aue, T., Balconi, M., Bylsma, L. M., Critchley, H., Demaree,
H. A., Friedman, B. H., Gooding, A. E. K., Gosseries, O., Jovanovic, T., Kirby, L. A. J.,
Kozlowska, K., Laureys, S., Lowe, L., Magee, K., Marin, M.-F., Merner, A. R.,
Robinson, J. L., … VanElzakker, M. B. (2019). Physiological feelings. Neuroscience &
Biobehavioral Reviews, 103, 267–304. https://doi.org/10.1016/j.neubiorev.2019.05.002
Page, A. C. (1994). Blood-injury phobia. Clinical Psychology Review, 14(5), 443–461.
https://doi.org/10.1016/0272-7358(94)90036-1
Palomero-Gallagher, N., & Amunts, K. (2022). A short review on emotion processing: A
lateralized network of neuronal networks. Brain Structure and Function, 227(2), 673–
684. https://doi.org/10.1007/s00429-021-02331-7
Parkinson, C., Sinnott-Armstrong, W., Koralus, P. E., Mendelovici, A., McGeer, V., &
Wheatley, T. (2011). Is Morality Unified? Evidence that Distinct Neural Systems
Underlie Moral Judgments of Harm, Dishonesty, and Disgust. Journal of Cognitive
Neuroscience, 23(10), 3162–3180. https://doi.org/10.1162/jocn_a_00017
Pascual, L., Gallardo-Pujol, D., & Rodrigues, P. (2013). How does morality work in the brain? A
functional and structural perspective of moral behavior. Frontiers in Integrative
Neuroscience, 7. https://doi.org/10.3389/fnint.2013.00065
Patil, I., Melsbach, J., Hennig-Fast, K., & Silani, G. (2016). Divergent roles of autistic and
alexithymic traits in utilitarian moral judgments in adults with autism. Scientific Reports,
6(1), Article 1. https://doi.org/10.1038/srep23637
Patriquin, M. A., DeRamus, T., Libero, L. E., Laird, A., & Kana, R. K. (2016). Neuroanatomical
and neurofunctional markers of social cognition in autism spectrum disorder. Human
Brain Mapping, 37(11), 3957–3978. https://doi.org/10.1002/hbm.23288
Pelphrey, K. A., Morris, J. P., McCarthy, G., & LaBar, K. S. (2007). Perception of dynamic
changes in facial affect and identity in autism. Social Cognitive and Affective
Neuroscience, 2(2), 140–149. https://doi.org/10.1093/scan/nsm010
Penfield, W., & Faulk, M. E., Jr. (1955). The Insula: Further Observations On Its Function.
202
Brain, 78(4), 445–470. https://doi.org/10.1093/brain/78.4.445
Phillips, M. L., Senior, C., Fahy, T., & David, A. S. (1998). Disgust – the forgotten emotion of
psychiatry. The British Journal of Psychiatry, 172(5), 373–375.
https://doi.org/10.1192/bjp.172.5.373
Piaget, J. (1965). The Moral Judgment of the Child.(Translated by Marjorie Gabain). Routledge
& K. Paul (1965, 1932).
Pierce, K., & Redcay, E. (2008). Fusiform Function in Children with an Autism Spectrum
Disorder Is a Matter of “Who.” Biological Psychiatry, 64(7), 552–560.
https://doi.org/10.1016/j.biopsych.2008.05.013
Piggot, J., Kwon, H., Mobbs, D., Blasey, C., Lotspeich, L., Menon, V., Bookheimer, S., & Reiss,
A. L. (2004). Emotional Attribution in High-Functioning Individuals With Autistic
Spectrum Disorder: A Functional Imaging Study. Journal of the American Academy of
Child & Adolescent Psychiatry, 43(4), 473–480. https://doi.org/10.1097/00004583-
200404000-00014
Pitskel, N. B., Bolling, D. Z., Hudac, C. M., Lantz, S. D., Minshew, N. J., Vander Wyk, B. C., &
Pelphrey, K. A. (2011). Brain Mechanisms for Processing Direct and Averted Gaze in
Individuals with Autism. Journal of Autism and Developmental Disorders, 41(12), 1686–
1693. https://doi.org/10.1007/s10803-011-1197-x
Pitskel, N. B., Bolling, D. Z., Kaiser, M. D., Pelphrey, K. A., & Crowley, M. J. (2014). Neural
systems for cognitive reappraisal in children and adolescents with autism spectrum
disorder. Developmental Cognitive Neuroscience, 10, 117–128.
https://doi.org/10.1016/j.dcn.2014.08.007
Pond Jr., R. S., DeWall, C. N., Lambert, N. M., Deckman, T., Bonser, I. M., & Fincham, F. D.
(2012). Repulsed by violence: Disgust sensitivity buffers trait, behavioral, and daily
aggression. Journal of Personality and Social Psychology, 102(1), 175–188.
https://doi.org/10.1037/a0024296
Poppa, T., & Bechara, A. (2018). The somatic marker hypothesis: Revisiting the role of the
‘body-loop’ in decision-making. Current Opinion in Behavioral Sciences, 19, 61–66.
https://doi.org/10.1016/j.cobeha.2017.10.007
Porges, S. (1993). Body perception questionnaire. Laboratory of Developmental Assessment,
University of Maryland.
Power, M., & Dalgleish, T. (2015). Cognition and Emotion: From order to disorder. Psychology
Press.
Pritchard, T. C., Macaluso, D. A., & Eslinger, P. J. (1999). Taste perception in patients with
insular cortex lesions. Behavioral Neuroscience, 113(4), 663–671.
Pruim, R. H. R., Mennes, M., van Rooij, D., Llera, A., Buitelaar, J. K., & Beckmann, C. F.
203
(2015). ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from
fMRI data. NeuroImage, 112, 267–277.
https://doi.org/10.1016/j.neuroimage.2015.02.064
R Core Team. (2013). R: A language and environment for statistical computing. https://www.Rproject.org/
Rachman, S. J. (1990). Fear and courage, 2nd ed (pp. xii, 405). W H Freeman/Times Books/
Henry Holt & Co.
Rakoczy, H., Kaufmann, M., & Lohse, K. (2016). Young children understand the normative
force of standards of equal resource distribution. Journal of Experimental Child
Psychology, 150, 396–403. https://doi.org/10.1016/j.jecp.2016.05.015
Reilly, M. (1962). Occupational therapy can be one of the great ideas of 20th century medicine.
The American Journal of Occupational Therapy : Official Publication of the American
Occupational Therapy Association. https://doi.org/10.1177/000841746303000102
Reilly, M. (1966). A psychiatric occupational therapy program as a teaching model. The
American Journal of Occupational Therapy: Official Publication of the American
Occupational Therapy Association, 20(2), 61–67.
Reyes, B. M.-A. (2019). Disgust as a Protective Emotion of Physical and Mental Health.
International Journal of Innovative Science and Research Technology, 4(12).
Reynolds, G., & Askew, C. (2019). Effects of vicarious disgust learning on the development of
fear, disgust, and attentional biases in children. Emotion (Washington, D.C.), 19(7),
1268–1283. https://doi.org/10.1037/emo0000511
Reynolds, L. M., Consedine, N. S., Pizarro, D. A., & Bissett, I. P. (2013). Disgust and
Behavioral Avoidance in Colorectal Cancer Screening and Treatment: A Systematic
Review and Research Agenda. Cancer Nursing, 36(2), 122–130.
https://doi.org/10.1097/NCC.0b013e31826a4b1b
Rieffe, C., Oosterveld, P., & Terwogt, M. M. (2006). An alexithymia questionnaire for children:
Factorial and concurrent validation results. Personality and Individual Differences, 40(1),
123–133. https://doi.org/10.1016/j.paid.2005.05.013
Ristori, M. V., Quagliariello, A., Reddel, S., Ianiro, G., Vicari, S., Gasbarrini, A., & Putignani,
L. (2019). Autism, Gastrointestinal Symptoms and Modulation of Gut Microbiota by
Nutritional Interventions. Nutrients, 11(11), Article 11.
https://doi.org/10.3390/nu11112812
Rizzolatti, G., & Sinigaglia, C. (2016). The mirror mechanism: A basic principle of brain
function. Nature Reviews Neuroscience, 17(12), Article 12.
https://doi.org/10.1038/nrn.2016.135
Royet, J. P., Hudry, J., Zald, D. H., Godinot, D., Grégoire, M. C., Lavenne, F., Costes, N., &
204
Holley, A. (2001). Functional Neuroanatomy of Different Olfactory Judgments.
NeuroImage, 13(3), 506–519. https://doi.org/10.1006/nimg.2000.0704
Rozin, P. (1990). Development in the food domain. Developmental Psychology, 26(4), 555–562.
https://doi.org/10.1037/0012-1649.26.4.555
Rozin, P., & Fallon, A. E. (1987). A perspective on disgust. Psychological Review, 94(1), 23–41.
https://doi.org/10.1037/0033-295X.94.1.23
Rozin, P., Fallon, A., & Mandell, R. (1984). Family resemblance in attitudes to foods.
Developmental Psychology, 20(2), 309–314. https://doi.org/10.1037/0012-1649.20.2.309
Rozin, P., Haidt, J., McCauley, C., Dunlop, L., & Ashmore, M. (1999). Individual Differences in
Disgust Sensitivity: Comparisons and Evaluations of Paper-and-Pencil versus Behavioral
Measures. Journal of Research in Personality, 33(3), 330–351.
https://doi.org/10.1006/jrpe.1999.2251
Rozin, P., Haidt, J., & McCauley, C. R. (1999). Disgust: The body and soul emotion. Handbook
of Cognition and Emotion, 429, 445.
Rozin, P., Haidt, J., & McCauley, C. R. (2008). Disgust. In Handbook of emotions, 3rd ed (pp.
757–776). The Guilford Press.
Rozin, P., Lowery, L., Imada, S., & Haidt, J. (1999). The CAD triad hypothesis: A mapping
between three moral emotions (contempt, anger, disgust) and three moral codes
(community, autonomy, divinity). Journal of Personality and Social Psychology, 76(4),
574–586. https://doi.org/10.1037/0022-3514.76.4.574
Rozin, P., Markwith, M., & McCauley, C. (1994). Sensitivity to indirect contacts with other
persons: AIDS aversion as a composite of aversion to strangers, infection, moral taint,
and misfortune. Journal of Abnormal Psychology, 103(3), 495–504.
https://doi.org/10.1037//0021-843x.103.3.495
Rozin, P., & Millman, L. (1987). Family environment, not heredity, accounts for family
resemblances in food preferences and attitudes: A twin study. Appetite, 8(2), 125–134.
https://doi.org/10.1016/S0195-6663(87)80005-3
Rueda, P., Fernández-Berrocal, P., & Baron-Cohen, S. (2015). Dissociation between cognitive
and affective empathy in youth with Asperger Syndrome. European Journal of
Developmental Psychology, 12(1), 85–98.
https://doi.org/10.1080/17405629.2014.950221
Russo, N., Nicol, T., Trommer, B., Zecker, S., & Kraus, N. (2009). Brainstem transcription of
speech is disrupted in children with autism spectrum disorders. Developmental Science,
12(4), 557–567. https://doi.org/10.1111/j.1467-7687.2008.00790.x
Saarimäki, H., Glerean, E., Smirnov, D., Mynttinen, H., Jääskeläinen, I. P., Sams, M., &
Nummenmaa, L. (2022). Classification of emotion categories based on functional
205
connectivity patterns of the human brain. NeuroImage, 247, 118800.
https://doi.org/10.1016/j.neuroimage.2021.118800
Safar, K., Vandewouw, M. M., & Taylor, M. J. (2021). Atypical development of emotional face
processing networks in autism spectrum disorder from childhood through to adulthood.
Developmental Cognitive Neuroscience, 51, 101003.
https://doi.org/10.1016/j.dcn.2021.101003
Saive, A.-L., Royet, J.-P., & Plailly, J. (2014). A review on the neural bases of episodic odor
memory: From laboratory-based to autobiographical approaches. Frontiers in Behavioral
Neuroscience, 0. https://doi.org/10.3389/fnbeh.2014.00240
Salvano-Pardieu, V., Blanc, R., Combalbert, N., Pierratte, A., Manktelow, K., Maintier, C.,
Lepeltier, S., Gimenes, G., Barthelemy, C., & Fontaine, R. (2016). Judgment of blame in
teenagers with Asperger’s syndrome. Thinking & Reasoning, 22(3), 251–273.
https://doi.org/10.1080/13546783.2015.1127288
Sambataro, F., Dimalta, S., Di Giorgio, A., Taurisano, P., Blasi, G., Scarabino, T., Giannatempo,
G., Nardini, M., & Bertolino, A. (2006). Preferential responses in amygdala and insula
during presentation of facial contempt and disgust. European Journal of Neuroscience,
24(8), 2355–2362. https://doi.org/10.1111/j.1460-9568.2006.05120.x
Sanfey, A. G., Rilling, J. K., Aronson, J. A., Nystrom, L. E., & Cohen, J. D. (2003). The Neural
Basis of Economic Decision-Making in the Ultimatum Game. Science, 300(5626), 1755–
1758. https://www.jstor.org/stable/3834595
Sarinopoulos, I., Grupe, D. W., Mackiewicz, K. L., Herrington, J. D., Lor, M., Steege, E. E., &
Nitschke, J. B. (2010). Uncertainty during anticipation modulates neural responses to
aversion in human insula and amygdala. Cerebral Cortex (New York, N.Y.: 1991), 20(4),
929–940. https://doi.org/10.1093/cercor/bhp155
Scarpazza, C., Làdavas, E., & Pellegrino, G. di. (2015). Dissociation between Emotional
Remapping of Fear and Disgust in Alexithymia. PLOS ONE, 10(10), e0140229.
https://doi.org/10.1371/journal.pone.0140229
Schaal, B., Saxton, T. K., Loos, H., Soussignan, R., & Durand, K. (2020). Olfaction scaffolds the
developing human from neonate to adolescent and beyond. Philosophical Transactions of
the Royal Society B: Biological Sciences, 375(1800), 20190261.
https://doi.org/10.1098/rstb.2019.0261
Schäfer, A., Schienle, A., & Vaitl, D. (2005). Stimulus type and design influence hemodynamic
responses towards visual disgust and fear elicitors. International Journal of
Psychophysiology, 57(1), 53–59. https://doi.org/10.1016/j.ijpsycho.2005.01.011
Schaich Borg, J., Lieberman, D., & Kiehl, K. A. (2008). Infection, Incest, and Iniquity:
Investigating the Neural Correlates of Disgust and Morality. Journal of Cognitive
Neuroscience, 20(9), 1529–1546. https://doi.org/10.1162/jocn.2008.20109
206
Schandry, R. (1981). Heart beat perception and emotional experience. Psychophysiology, 18(4),
483–488. https://doi.org/10.1111/j.1469-8986.1981.tb02486.x
Schkade, J. K., & Schultz, S. (2003). Occupational adaptation. Perspectives in Human
Occupation: Participation in Life, 181–221.
Schmitt, L., Heiss, C. J., & Campbell, E. E. (2008). A Comparison of Nutrient Intake and Eating
Behaviors of Boys With and Without Autism. Topics in Clinical Nutrition, 23(1), 23.
https://doi.org/10.1097/01.TIN.0000312077.45953.6c
Schnall, S., Benton, J., & Harvey, S. (2008). With a Clean Conscience: Cleanliness Reduces the
Severity of Moral Judgments. Psychological Science, 19(12), 1219–1222.
https://doi.org/10.1111/j.1467-9280.2008.02227.x
Schnall, S., Haidt, J., Clore, G. L., & Jordan, A. H. (2008). Disgust as Embodied Moral
Judgment. Personality & Social Psychology Bulletin, 34(8), 1096–1109.
https://doi.org/10.1177/0146167208317771
Schneider, K., Pauly, K. D., Gossen, A., Mevissen, L., Michel, T. M., Gur, R. C., Schneider, F.,
& Habel, U. (2013). Neural correlates of moral reasoning in autism spectrum disorder.
Social Cognitive and Affective Neuroscience, 8(6), 702–710.
https://doi.org/10.1093/scan/nss051
Scime, M., & Norvilitis, J. M. (2006). Task performance and response to frustration in children
with attention deficit hyperactivity disorder. Psychology in the Schools, 43(3), 377–386.
https://doi.org/10.1002/pits.20151
Scott, S. (2019). Explication of moral disgust: Assessing physiological and behavioral responses
to disgust eliciting videos. Electronic Theses and Dissertations.
https://egrove.olemiss.edu/etd/1784
Senju, A., & Csibra, G. (2008). Gaze following in human infants depends on communicative
signals. Current Biology: CB, 18(9), 668–671. https://doi.org/10.1016/j.cub.2008.03.059
Senju, A., Southgate, V., White, S., & Frith, U. (2009). Mindblind Eyes: An Absence of
Spontaneous Theory of Mind in Asperger Syndrome. Science, 325(5942), 883–885.
https://doi.org/10.1126/science.1176170
Shenhav, A., & Mendes, W. B. (2014). Aiming for the stomach and hitting the heart: Dissociable
triggers and sources for disgust reactions. Emotion, 14(2), 301–309.
https://doi.org/10.1037/a0034644
Sherlock, J. M., Zietsch, B. P., Tybur, J. M., & Jern, P. (2016). The quantitative genetics of
disgust sensitivity. Emotion, 16(1), 43–51. https://doi.org/10.1037/emo0000101
Shoemaker, W. J. (2012). The Social Brain Network and Human Moral Behavior. Zygon®,
47(4), 806–820. https://doi.org/10.1111/j.1467-9744.2012.01295.x
207
Shook, N. J., Oosterhoff, B., Terrizzi Jr., J. A., & Brady, K. M. (2017). “Dirty politics”: The role
of disgust sensitivity in voting. Translational Issues in Psychological Science, 3(3), 284–
297. https://doi.org/10.1037/tps0000111
Sica, C., Caudek, C., Belloch, A., Bottesi, G., Ghisi, M., Melli, G., García-Soriano, G., &
Olatunji, B. O. (2019). Not Just Right Experiences, Disgust Proneness and Their
Associations to Obsessive–Compulsive Symptoms: A Stringent Test with Structural
Equation Modeling Analysis. Cognitive Therapy and Research, 43(6), 1086–1096.
https://doi.org/10.1007/s10608-019-10029-8
Siegal, M. (2008). Marvelous Minds: The Discovery of what Children Know. Oxford University
Press.
Siegal, M., Fadda, R., & Overton, P. G. (2011). Contamination sensitivity and the development
of disease-avoidant behaviour. Philosophical Transactions of the Royal Society B:
Biological Sciences, 366(1583), 3427–3432. https://doi.org/10.1098/rstb.2011.0036
Siegal, M., & Share, D. L. (1990). Contamination sensitivity in young children. Developmental
Psychology, 26(3), 455–458. https://doi.org/10.1037/0012-1649.26.3.455
Sifneos, P. E. (1973). The prevalence of “alexithymic” characteristics in psychosomatic patients.
Psychotherapy and Psychosomatics, 22(2–6), 255–262.
https://doi.org/10.1159/000286529
Sigar, P., Uddin, L. Q., & Roy, D. (2023). Altered global modular organization of intrinsic
functional connectivity in autism arises from atypical node-level processing. Autism
Research, 16(1), 66–83. https://doi.org/10.1002/aur.2840
Singh, N. N., Lancioni, G. E., Manikam, R., Winton, A. S. W., Singh, A. N. A., Singh, J., &
Singh, A. D. A. (2011). A mindfulness-based strategy for self-management of aggressive
behavior in adolescents with autism. Research in Autism Spectrum Disorders, 5(3),
1153–1158. https://doi.org/10.1016/j.rasd.2010.12.012
Sledge, W. H. (1978). Antecedent psychological factors in the onset of vasovagal syncope.
Psychosomatic Medicine, 40(7), 568–579. https://doi.org/10.1097/00006842-197811000-
00004
Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E. J., JohansenBerg, H., Bannister, P. R., De Luca, M., Drobnjak, I., Flitney, D. E., Niazy, R. K.,
Saunders, J., Vickers, J., Zhang, Y., De Stefano, N., Brady, J. M., & Matthews, P. M.
(2004). Advances in functional and structural MR image analysis and implementation as
FSL. NeuroImage, 23 Suppl 1, S208-219.
https://doi.org/10.1016/j.neuroimage.2004.07.051
South, M., & Rodgers, J. (2017). Sensory, Emotional and Cognitive Contributions to Anxiety in
Autism Spectrum Disorders. Frontiers in Human Neuroscience, 11.
https://www.frontiersin.org/articles/10.3389/fnhum.2017.00020
208
Spinelli, S., Cunningham, C., Pierguidi, L., Dinnella, C., Monteleone, E., & White, T. L. (2021).
The relationship between disgust sensitivity and BMI: Is the food disgusting or am I?
Food Quality and Preference, 92, 104222.
https://doi.org/10.1016/j.foodqual.2021.104222
Sprengelmeyer, R., Young, A. W., Pundt, I., Sprengelmeyer, A., Calder, A. J., Berrios, G.,
Winkel, R., Vollmöeller, W., Kuhn, W., Sartory, G., & Przuntek, H. (1997). Disgust
implicated in obsessive–compulsive disorder. Proceedings of the Royal Society of
London. Series B: Biological Sciences, 264(1389), 1767–1773.
https://doi.org/10.1098/rspb.1997.0245
Stafford, L. D., Tsang, I., López, B., Severini, M., & Iacomini, S. (2017). Autistic traits
associated with food neophobia but not olfactory sensitivity. Appetite, 116, 584–588.
https://doi.org/10.1016/j.appet.2017.05.054
Stark, R., Schienle, A., Walter, B., Kirsch, P., Sammer, G., Ott, U., Blecker, C., & Vaitl, D.
(2003). Hemodynamic responses to fear and disgust-inducing pictures: An fMRI study.
International Journal of Psychophysiology, 50(3), 225–234.
https://doi.org/10.1016/S0167-8760(03)00169-7
Stephani, C., Fernandez-Baca Vaca, G., Maciunas, R., Koubeissi, M., & Lüders, H. O. (2011).
Functional neuroanatomy of the insular lobe. Brain Structure & Function, 216(2), 137–
149. https://doi.org/10.1007/s00429-010-0296-3
Stevenson, R. J., Miller, L. A., & McGrillen, K. (2015). Perception of odor-induced tastes
following insular cortex lesion. Neurocase, 21(1), 33–43.
https://doi.org/10.1080/13554794.2013.860175
Stevenson, R. J., Oaten, M. J., Case, T. I., Repacholi, B. M., & Wagland, P. (2010). Children’s
response to adult disgust elicitors: Development and acquisition. Developmental
Psychology, 46(1), 165–177. https://doi.org/10.1037/a0016692
Sukhodolsky, D. G., Wyk, B. C. V., Eilbott, J. A., McCauley, S. A., Ibrahim, K., Crowley, M. J.,
& Pelphrey, K. A. (2016). Neural Mechanisms of Cognitive-Behavioral Therapy for
Aggression in Children and Adolescents: Design of a Randomized Controlled Trial
Within the National Institute for Mental Health Research Domain Criteria Construct of
Frustrative Non-Reward. Journal of Child and Adolescent Psychopharmacology, 26(1),
38–48. https://doi.org/10.1089/cap.2015.0164
Suzman, E., Williams, Z. J., Feldman, J. I., Failla, M., Cascio, C. J., Wallace, M. T., Niarchou,
M., Sutcliffe, J. S., Wodka, E., & Woynaroski, T. G. (2021). Psychometric validation and
refinement of the Interoception Sensory Questionnaire (ISQ) in adolescents and adults on
the autism spectrum. Molecular Autism, 12(1), 42. https://doi.org/10.1186/s13229-021-
00440-y
Takahashi, Y., Murata, S., Idei, H., Tomita, H., & Yamashita, Y. (2021). Neural network
modeling of altered facial expression recognition in autism spectrum disorders based on
predictive processing framework. Scientific Reports, 11(1), Article 1.
209
https://doi.org/10.1038/s41598-021-94067-x
Tiggemann, M., & Lewis, C. (2004). Attitudes Toward Women’s Body Hair: Relationship with
Disgust Sensitivity. Psychology of Women Quarterly, 28(4), 381–387.
https://doi.org/10.1111/j.1471-6402.2004.00155.x
Tompkins, S. S. (1963). Affect, imagery, consciousness: II. The Negative Affects (p. 580).
Springer.
Toronchuk, J. A., & Ellis, G. F. R. (2007). Disgust: Sensory affect or primary emotional system?
Cognition and Emotion, 21(8), 1799–1818. https://doi.org/10.1080/02699930701298515
Tottenham, N., Tanaka, J. W., Leon, A. C., McCarry, T., Nurse, M., Hare, T. A., Marcus, D. J.,
Westerlund, A., Casey, B., & Nelson, C. (2009). The NimStim set of facial expressions:
Judgments from untrained research participants. Psychiatry Research, 168(3), 242–249.
https://doi.org/10.1016/j.psychres.2008.05.006
Tracy, J. L., Steckler, C. M., & Heltzel, G. (2019). The physiological basis of psychological
disgust and moral judgments. Journal of Personality and Social Psychology, 116(1), 15–
32. https://doi.org/10.1037/pspa0000141
Turiel, E. (1983). The Development of Social Knowledge: Morality and Convention. Cambridge
University Press.
Tybur, J. M., Merriman, L. A., Hooper, A. E. C., McDonald, M. M., & Navarrete, C. D. (2010).
Extending the Behavioral Immune System to Political Psychology: Are Political
Conservatism and Disgust Sensitivity Really Related? Evolutionary Psychology, 8(4),
147470491000800406. https://doi.org/10.1177/147470491000800406
Uddin, L. Q., Nomi, J. S., Hebert-Seropian, B., Ghaziri, J., & Boucher, O. (2017). Structure and
function of the human insula. Journal of Clinical Neurophysiology : Official Publication
of the American Electroencephalographic Society, 34(4), 300–306.
https://doi.org/10.1097/WNP.0000000000000377
Uljarevic, M., & Hamilton, A. (2013). Recognition of Emotions in Autism: A Formal MetaAnalysis. Journal of Autism and Developmental Disorders, 43(7), 1517–1526.
https://doi.org/10.1007/s10803-012-1695-5
Vaiouli, P., Luminet, O., & Panayiotou, G. (2022). Alexithymic and autistic traits in children and
adolescents: A systematic review of the current state of knowledge. Autism, 26(2), 308–
316. https://doi.org/10.1177/13623613211058512
van Overveld, M., de Jong, P. J., Peters, M. L., van Hout, W. J. P. J., & Bouman, T. K. (2008).
An internet-based study on the relation between disgust sensitivity and emetophobia.
Journal of Anxiety Disorders, 22(3), 524–531.
https://doi.org/10.1016/j.janxdis.2007.04.001
van Overveld, W. J. M., de Jong, P. J., & Peters, M. L. (2009). Digestive and cardiovascular
210
responses to core and animal-reminder disgust. Biological Psychology, 80(2), 149–157.
https://doi.org/10.1016/j.biopsycho.2008.08.002
van Overveld, W. J. M., de Jong, P. J., Peters, M. L., Cavanagh, K., & Davey, G. C. L. (2006).
Disgust propensity and disgust sensitivity: Separate constructs that are differentially
related to specific fears. Personality and Individual Differences, 41(7), 1241–1252.
https://doi.org/10.1016/j.paid.2006.04.021
Vasiljevic, M., & Crisp, R. J. (2013). Tolerance by Surprise: Evidence for a Generalized
Reduction in Prejudice and Increased Egalitarianism through Novel Category
Combination. PLOS ONE, 8(3), e57106. https://doi.org/10.1371/journal.pone.0057106
Vernet-Maury, E., Alaoui-Ismaı̈li, O., Dittmar, A., Delhomme, G., & Chanel, J. (1999). Basic
emotions induced by odorants: A new approach based on autonomic pattern results.
Journal of the Autonomic Nervous System, 75(2), 176–183.
https://doi.org/10.1016/S0165-1838(98)00168-4
Viar-Paxton, M., & Olatunji, B. O. (2016). 21—Measurement of Disgust Proneness. In H. L.
Meiselman (Ed.), Emotion Measurement (pp. 513–535). Woodhead Publishing.
https://doi.org/10.1016/B978-0-08-100508-8.00021-7
Vicario, C. M. (2013). Altered Insula Response to Sweet Taste Processing in Recovered
Anorexia and Bulimia Nervosa: A Matter of Disgust Sensitivity? American Journal of
Psychiatry, 170(12), 1497–1497. https://doi.org/10.1176/appi.ajp.2013.13060748
Vicario, C. M., Rafal, R. D., Borgomaneri, S., Paracampo, R., Kritikos, A., & Avenanti, A.
(2017). Pictures of disgusting foods and disgusted facial expressions suppress the tongue
motor cortex. Social Cognitive and Affective Neuroscience, 12(2), 352–362.
https://doi.org/10.1093/scan/nsw129
Vicario, C. M., Rafal, R. D., Martino, D., & Avenanti, A. (2017). Core, social and moral disgust
are bounded: A review on behavioral and neural bases of repugnance in clinical
disorders. Neuroscience & Biobehavioral Reviews, 80, 185–200.
https://doi.org/10.1016/j.neubiorev.2017.05.008
Vrana, S. R. (1993). The psychophysiology of disgust: Differentiating negative emotional
contexts with facial EMG. Psychophysiology, 30(3), 279–286.
https://doi.org/10.1111/j.1469-8986.1993.tb03354.x
Vyas, K., Jameel, L., Bellesi, G., Crawford, S., & Channon, S. (2017). Derailing the trolley:
Everyday utilitarian judgments in groups high versus low in psychopathic traits or autistic
traits. Psychiatry Research, 250, 84–91. https://doi.org/10.1016/j.psychres.2017.01.054
Wagemans, F. M. A., Brandt, M. J., & Zeelenberg, M. (2018). Disgust sensitivity is primarily
associated with purity-based moral judgments. Emotion (Washington, D.C.), 18(2), 277–
289. https://doi.org/10.1037/emo0000359
Wagner, S., Issanchou, S., Chabanet, C., Lange, C., Schaal, B., & Monnery-Patris, S. (2019).
211
Weanling Infants Prefer the Odors of Green Vegetables, Cheese, and Fish When Their
Mothers Consumed These Foods During Pregnancy and/or Lactation. Chemical Senses,
44(4), 257–265. https://doi.org/10.1093/chemse/bjz011
Wang, A. T., Dapretto, M., Hariri, A. R., Sigman, M., & Bookheimer, S. Y. (2004). Neural
Correlates of Facial Affect Processing in Children and Adolescents With Autism
Spectrum Disorder. Journal of the American Academy of Child & Adolescent Psychiatry,
43(4), 481–490. https://doi.org/10.1097/00004583-200404000-00015
Watkins, T. J., Di Iorio, C. R., Olatunji, B. O., Benningfield, M. M., Blackford, J. U., Dietrich,
M. S., Bhatia, M., Theiss, J. D., Salomon, R. M., Niswender, K., & Cowan, R. L. (2016).
Disgust proneness and associated neural substrates in obesity. Social Cognitive and
Affective Neuroscience, 11(3), 458–465. https://doi.org/10.1093/scan/nsv129
Watson, L. R., Baranek, G. T., & DiLavore, P. C. (2003). Toddlers With Autism: Developmental
Perspectives. Infants & Young Children, 16(3), 201–214.
https://journals.lww.com/iycjournal/fulltext/2003/07000/toddlers_with_autism__develop
mental_perspectives.3.aspx?casa_token=qDoSoANIzngAAAAA:i2dhrLKyzpFNXtbm1T
cqHnKJtRIEUfIzPf-SAaZVJnbfuuOOKfdXN6qr1o6NcHJHeK5xwdELqgG0uY9dZBsLhA
Wechsler, D. (2011). WASI-II: Wechsler abbreviated scale of intelligence. PsychCorp.
Wheatley, T., & Haidt, J. (2005). Hypnotic disgust makes moral judgments more severe.
Psychological Science, 16(10), 780–784. https://doi.org/10.1111/j.1467-
9280.2005.01614.x
White, S. W., Maddox, B. B., & Panneton, R. K. (2015). Fear of Negative Evaluation Influences
Eye Gaze in Adolescents with Autism Spectrum Disorder: A Pilot Study. Journal of
Autism and Developmental Disorders, 45(11), 3446–3457.
https://doi.org/10.1007/s10803-014-2349-6
Whitton, A. E., Grisham, J. R., Henry, J. D., & Palada, H. D. (2013). Interpretive Bias
Modification for Disgust. Journal of Experimental Psychopathology, 4(4), 341–359.
https://doi.org/10.5127/jep.030812
Wicker, B., Keysers, C., Plailly, J., Royet, J.-P., Gallese, V., & Rizzolatti, G. (2003). Both of Us
Disgusted in My Insula: The Common Neural Basis of Seeing and Feeling Disgust.
Neuron, 40(3), 655–664. https://doi.org/10.1016/S0896-6273(03)00679-2
Wicker, B., Monfardini, E., & Royet, J.-P. (2016). Olfactory processing in adults with autism
spectrum disorders. Molecular Autism, 7(1), 4. https://doi.org/10.1186/s13229-016-0070-
3
Widen, S. C., & Russell, J. A. (2013). Children’s recognition of disgust in others. Psychological
Bulletin, 139(2), 271–299. https://doi.org/10.1037/a0031640
Wilcock, A. (1991). We are what we do: An occupational perspective on life, health and the
212
profession. 73–93.
Wilcock, A. A. (2002). Reflections on doing, being and becoming*: Reflections on doing, being
and becoming. Australian Occupational Therapy Journal, 46(1), 1–11.
https://doi.org/10.1046/j.1440-1630.1999.00174.x
Willard, H. S., & Schell, B. A. B. (2014). Willard & Spackman’s occupational therapy. Wolters
Kluwer Health/Lippincott Williams & Wilkins.
Winterich, K. P., Mittal, V., & Morales, A. C. (2014). Protect thyself: How affective selfprotection increases self-interested, unethical behavior. Organizational Behavior and
Human Decision Processes, 125(2), 151–161.
https://doi.org/10.1016/j.obhdp.2014.07.004
Wolf, R. C., Philippi, C. L., Motzkin, J. C., Baskaya, M. K., & Koenigs, M. (2014).
Ventromedial prefrontal cortex mediates visual attention during facial emotion
recognition. Brain, 137(6), 1772–1780. https://doi.org/10.1093/brain/awu063
Woolley, J., Strobl, E. V., Sturm, V. E., Shany-Ur, T., Poorzand, P., Grossman, S., Nguyen, L.,
Eckart, J. A., Levenson, R. W., Seeley, W. W., Miller, B. L., & Rankin, K. P. (2015).
Impaired recognition and regulation of disgust is associated with distinct but partially
overlapping patterns of decreased gray matter volume in the ventroanterior insula.
Biological Psychiatry, 78(7), 505–514. https://doi.org/10.1016/j.biopsych.2014.12.031
Woolrich, M. W., Behrens, T. E. J., Beckmann, C. F., Jenkinson, M., & Smith, S. M. (2004).
Multilevel linear modelling for FMRI group analysis using Bayesian inference.
NeuroImage, 21(4), 1732–1747. https://doi.org/10.1016/j.neuroimage.2003.12.023
Xu, L., Ma, X., Zhao, W., Luo, L., Yao, S., & Kendrick, K. M. (2015). Oxytocin enhances
attentional bias for neutral and positive expression faces in individuals with higher
autistic traits. Psychoneuroendocrinology, 62, 352–358.
https://doi.org/10.1016/j.psyneuen.2015.09.002
Yang, W. F. Z., Toller, G., Shdo, S., Kotz, S. A., Brown, J., Seeley, W. W., Kramer, J. H.,
Miller, B. L., & Rankin, K. P. (2021). Resting functional connectivity in the semantic
appraisal network predicts accuracy of emotion identification. NeuroImage: Clinical, 31,
102755. https://doi.org/10.1016/j.nicl.2021.102755
Yerxa, E. J. (1990). An Introduction to Occupational Science, A Foundation for Occupational
Therapy in the 21st Century. Occupational Therapy In Health Care, 6(4), 1–17.
https://doi.org/10.1080/J003v06n04_04
Yeung, M. K. (2022). A systematic review and meta-analysis of facial emotion recognition in
autism spectrum disorder: The specificity of deficits and the role of task characteristics.
Neuroscience & Biobehavioral Reviews, 133, 104518.
https://doi.org/10.1016/j.neubiorev.2021.104518
Yeung, M. K., Lee, T. L., & Chan, A. S. (2020). Impaired Recognition of Negative Facial
213
Expressions is Partly Related to Facial Perception Deficits in Adolescents with HighFunctioning Autism Spectrum Disorder. Journal of Autism and Developmental
Disorders, 50(5), 1596–1606. https://doi.org/10.1007/s10803-019-03915-3
Young, L., Camprodon, J. A., Hauser, M., Pascual-Leone, A., & Saxe, R. (2010). Disruption of
the right temporoparietal junction with transcranial magnetic stimulation reduces the role
of beliefs in moral judgments. Proceedings of the National Academy of Sciences of the
United States of America, 107(15), 6753–6758. https://doi.org/10.1073/pnas.0914826107
Zajonc, R. B., & McIntosh, D. N. (1992). Emotions Research: Some Promising Questions and
Some Questionable Promises. Psychological Science, 3(1), 70–74.
https://doi.org/10.1111/j.1467-9280.1992.tb00261.x
Zalla, T., Barlassina, L., Buon, M., & Leboyer, M. (2011). Moral judgment in adults with autism
spectrum disorders. Cognition, 121(1), 115–126.
https://doi.org/10.1016/j.cognition.2011.06.004
Zalla, T., & Leboyer, M. (2011). Judgment of intentionality and moral evaluation in individuals
with high functioning autism. Review of Philosophy and Psychology, 2(4), 681–698.
https://doi.org/10.1007/s13164-011-0048-1
Zeidan, J., Fombonne, E., Scorah, J., Ibrahim, A., Durkin, M. S., Saxena, S., Yusuf, A., Shih, A.,
& Elsabbagh, M. (2022). Global prevalence of autism: A systematic review update.
Autism Research, 15(5), 778–790. https://doi.org/10.1002/aur.2696
Zemestani, M., Hoseinpanahi, O., Salehinejad, M. A., & Nitsche, M. A. (2022). The impact of
prefrontal transcranial direct current stimulation (tDCS) on theory of mind, emotion
regulation and emotional-behavioral functions in children with autism disorder: A
randomized, sham-controlled, and parallel-group study. Autism Research, 15(10), 1985–
2003. https://doi.org/10.1002/aur.2803
Zhao, X., Zhang, P., Fu, L., & Maes, J. H. R. (2016). Attentional biases to faces expressing
disgust in children with autism spectrum disorders: An exploratory study. Scientific
Reports, 6(1), Article 1. https://doi.org/10.1038/srep19381
Zheng, Y., You, Y., Farias, A. R., Simon, J., Semin, G. R., Smeets, M. A., & Li, W. (2018).
Human chemosignals of disgust facilitate food judgment. Scientific Reports, 8(1), Article
1. https://doi.org/10.1038/s41598-018-35132-w
214
APPENDICES
Appendix i: Supplementary Figures
Supplementary Figure 21. Neural regions from the NeuroSynth meta-analysis maps, including
structural parcellations of insular sub-regions (negative slices = left side, positive slices = right
side). Regions of interest (ROIs) included dorsal and ventral anterior insula (slices -40 to -28,
and 30 to 42); mid-insula and posterior insula (slices -40, -34, 36, 42); anterior cingulate cortex
(slices -16, -10, 6, 12); amygdala (slices -28 to -10, and 6 to 36); medial orbitofrontal cortex and
ventromedial prefrontal cortex (slices -28 to 30); and fusiform areas (slices -40, -34, 36, 42).
215
Supplementary Figure 22. Psychophysiological interaction (PPI) effects during the Disgust Food
condition for the connectivity of A. the right mid-insula (MI), and B. the right medial
orbitofrontal cortex (mOFC).
216
Supplementary Figure 23. Psychophysiological interaction (PPI) effects during the Disgust
Faces condition for the connectivity of A. the right mid-insula (MI), and B. the left medial
orbitofrontal cortex (mOFC).
217
Appendix ii. Pilot Survey of Moral Foundations Theory (MFT) Questionnaire
1. Methods
1a. Participants
We primarily recruited non-autistic participants from our prior studies and used
advertising through word-of-mouth. All participants were between ages 8-17 years. We made our
best efforts to match the TD group for age and gender. Other inclusion criteria were: (a) no
history of ADHD; (b) no first degree relatives with ASD and no current or previous concerns
about an ASD diagnosis; (b) average verbal comprehension and cognitive functioning; (c) no
prior or concurrent diagnosis of other major neurological, development, and psychiatric disorders
mentioned during pre-screening of participants; and (d) English speaking children.
1b. Pilot survey
The pilot survey was distributed using Qualtrics. This survey comprised 113 vignettes -
63 moral vignettes from the MFT questionnaire, 25 physically disgusting vignettes, and 25
neutral negative vignettes. For the moral vignettes, participants were asked to rate how morally
wrong they felt the scenario was (5-point Likert scale), which moral foundation best described
the scenario (multiple choice), and their subjective account of why they felt the action was
wrong. For physically (open-ended). For physically disgusting vignettes, participants were asked
to rate how morally wrong they felt the action was (5-point Likert scale), and how disgusted they
felt thinking about the scenario (7-point Likert scale). For neutral negative vignettes, participants
were asked to rate how morally wrong they felt the action was (5-point Likert scale), and how
disgusted, sad, or angry they felt thinking about the scenario (7-point Likert scales each for
218
disgust, sadness and anger). An example for each of these vignettes and their questions can be
found in Supplementary Figure S4.
1c. Final selection of questions
For each of the moral vignettes, the median wrongness rating was calculated to determine
which questions elicited the strongest moral feelings amongst current school-going youth.
Additionally, for the questions that had the highest median wrongness ratings, a median of the
moral foundation associated with the question was calculated to supplement the selection of the
final set of questions.
For the physically disgusting vignettes, the median wrongness and disgust ratings were
calculated to determine which questions elicited the least moral feelings and the strongest
feelings of physical disgust respectively.
For the neutral negative vignettes, the median wrongness and disgust ratings were
calculated to determine which questions elicited the least moral feelings and the least feelings of
physical disgust respectively. Medians for the sadness and anger ratings were used to determine
which questions elicited the strongest feelings of sadness and anger (negative emotions
excluding disgust).
219
Supplementary Figure 24. Examples of pilot survey questions for moral violation vignettes (A),
physically disgusting vignettes (B), and neutral negative vignettes (C).
220
2. Results
A total of 15 participants filled out the survey, out of which only 11 participants
completed the survey. For each category of vignettes, a maximum of 15 questions were selected.
The final survey included the following list of vignettes:
2a. Moral vignettes
Training stimuli: “You see a man without a face mask sneeze in another woman’s face while
walking down the street”
Main stimuli:
● "You see a teenage boy laughing at an amputee he passes by while on the subway."
● "You see a woman commenting out loud about how fat another woman looks in her jeans."
● "You see a girl saying that another girl is too ugly to be a varsity cheerleader."
● "You see a woman swerving her car in order to intentionally run over a squirrel."
● "You see a boy setting a series of traps to kill stray cats in his neighborhood."
● "You see a boy placing a thumbtack sticking up on the chair of another student."
● "You see a woman spanking her child with a spatula for getting bad grades in school."
● "You see a teacher giving a bad grade to a student just because he dislikes him."
● "You see a family eating the dead body of their pet dog that had been run over."
● "You see a teenager urinating in the wave pool at a crowded water park."
● "You see a teenage male in a camp bathroom secretly using a stranger's toothbrush."
● "You see a lunch lady sweat into the cafeteria food and serve it to other students."
221
● "You see an employee at a morgue eating his pepperoni pizza off of a dead body."
● "You see a man blocking his wife from leaving home or interacting with others."
● "You see a teenager at a cafeteria forcing a younger student to pay for her lunch."
2b. Physically disgusting vignettes
● "You see a patient in the hospital having his leg amputated."
● "You smell the vomit on the floor of your classroom when you walk in."
● "You touch a cockroach."
● "You see moldy fruit in your fridge."
● "You smell cow dung when your family is driving through the countryside."
● "You taste milk that has gone bad."
● "You see a fly in your soup while having dinner in a restaurant."
● "You smell urine while walking down the street."
● "You see the rotting corpse of a dead squirrel."
● "You step in dog poop while you are walking in the park."
● "You see rats going through the garbage in an alley."
● "You see mold on a piece of bread you were about to eat."
● "You see a girl eat a mucus-covered snail."
● "You see a public toilet that has not been cleaned."
● "You see moldy fruit in the fruit aisle of the grocery store."
2c. Neutral negative vignettes
● "You watch a sad movie with a friend."
222
● "You fail a test in school."
● "You knock down your Lego structure that you spent time building."
● "You see your old pet dog pass away."
● "You attend a funeral of a family member."
● "You forget your lunch at home."
● "You hear about a big fight between two of your friends."
● "You see your friend has failed his final exam."
● "You forget to take your phone and leave it on the public bus."
● "You drop your phone in the pool."
● "You read a sad story on the internet."
● "You see your best friend crying after hurting their leg."
● "You hear about your friend’s cat passing away."
● "You see your gaming console crash and stop working."
● "You find out the grocery store is sold out of your favorite snacks."
223
Appendix iii. Analysis of covariance models for Disgust Sensitivity (DS), adjusting for
Gustatory sensitivity, Interoception, and Alexithymia
1. DS and gustatory sensitivity
For wrongness ratings, no significant estimator of differences was found across groups.
In separate analyses within the TD and ASD groups, we found that DS significantly estimated
the differences in wrongness ratings of purity violations (F=10.29, p=0.012, generalized partial
η2
=0.563) in the ASD group while adjusting for gustatory sensitivity, while no significant
estimator was found for the TD group.
For punishment ratings of purity vignettes, the clinical group (F=6.948, p=0.014,
generalized partial η2
=0.224) significantly estimated differences across participants while
adjusting for gustatory sensitivity. In separate analyses within the TD and ASD groups, we found
no significant estimators for punishment ratings.
For permissibility ratings of purity vignettes, no significant estimator of differences were
found across groups. In separate analyses within the TD and ASD groups, we found that DS
(F=5.65, p=0.041, generalized partial η2
=0.386) and the odor condition (F=5.399, p=0.045,
generalized partial η2
=0.375) significantly estimated the differences in permissibility ratings of
purity violations in the ASD group while adjusting for gustatory sensitivity, while no significant
estimator was found for the TD group.
2. DS and interoception (heartbeat counting)
For wrongness ratings, no significant estimator of differences was found across groups.
In separate analyses within the TD and ASD groups, we found that the odor condition
224
significantly estimated the differences in wrongness ratings of purity violations (F=8.994,
p=0.017, generalized partial η2
=0.529) in the ASD group while adjusting for mean absolute
difference in heartbeat counting/interoception, while no significant estimator was found for the
TD group.
For punishment ratings of purity vignettes, the clinical group (F=8.005, p=0.009,
generalized partial η2
=0.25) significantly estimated differences across participants while
adjusting for mean absolute difference in interoception. In separate analyses within the TD and
ASD groups, we found no significant estimators for punishment ratings.
For permissibility ratings of purity vignettes, interoception (F=5.708, p=0.025,
generalized partial η2
=0.199) significantly estimated differences across participants. In separate
analyses within the TD and ASD groups, we found that odor condition (F=10.89, p=0.009,
generalized partial η2
=0.547) significantly estimated the differences in permissibility ratings of
purity violations in the ASD group while adjusting for mean absolute difference in interoception,
while no significant estimator was found for the TD group.
3. DS and alexithymia
For wrongness ratings, DS significantly estimated the differences in wrongness ratings of
purity violations (F=4.422, p=0.047, generalized partial η2
=0.167) across participants. In
separate analyses within the TD and ASD groups, we found that DS (F=6.82, p=0.031,
generalized partial η2
=0.46) and the odor condition (F=5.453, p=0.048, generalized partial
η2
=0.405) significantly estimated the differences in wrongness ratings of purity violations in the
ASD group while adjusting for alexithymia, while no significant estimator was found for the TD
group.
225
For punishment ratings of purity vignettes, the clinical group (F=6.121, p=0.021,
generalized partial η2
=0.203) significantly estimated differences across participants while
adjusting for alexithymia. In separate analyses within the TD and ASD groups, we found no
significant estimators for punishment ratings.
For permissibility ratings of purity vignettes, no significant estimator of differences were
found across groups and within the TD and ASD groups. Although, for the ASD group, the odor
condition was found to be near significant (F=4.882, p=0.054, generalized partial η2
=0.352),
after adjusting for alexithymia.
Abstract (if available)
Abstract
Autistic youth exhibit divergent disgust processing, which may manifest as differences in behaviors towards contaminating stimuli (core/physical disgust) and differences in vicarious socio-emotional processing (social disgust). Furthermore, autistic youth tend to display relatively more outcome-based moral judgments, leading to stronger moral feelings and attributions of punishment for unintentional, unexplained moral violations. In non-autistic individuals, there is an established link between physical disgust processing and moral judgments (moral disgust), but the relationship remains unexplored in autism. This dissertation synthesized existing literature to underscore the potential consequences of differing disgust processing in autism, including increased susceptibility to illness and gastrointestinal issues, and restricted social communication and vicarious learning of disgust stimuli. In a series of three studies, this dissertation aimed to address this gap by elucidating the neurobiological basis of disgust processing differences in autistic youth in the first and second studies, and by investigating the linkage of disgust processing and moral judgments in autism in the third study. Our findings shed light on the nuanced activity and connectivity patterns underlying the physical and social disgust processing differences observed in autistic youth, especially concerning the right mid-insula; and delineated the influence of these physical disgust processing differences on feelings of moral disgust in autism. Moreover, by enhancing our understanding of disgust processing and its implications for social functioning, this study may pave the way for novel interventions aimed at improving the quality of life for autistic youth.
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Core, social and moral disgust processing in youth with autism spectrum disorder (ASD)
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