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Role of emotion and empathy in moral judgments of real-life situations
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Role of emotion and empathy in moral judgments of real-life situations
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Role of emotion and empathy in moral judgments of real-life situations
Vanessa
Dr. Antonio Damasio, Advisor
Dr. Mary Helen Immordino-Yang, Co-Advisor
a dissertation submitted to the
USC DEPARTMENT OF PSYCHOLOGY
UNIVERSITY OF SOUTHERN CALIFORNIA
in partial fulfillment of the
requirements for the degree of
DOCTOR OF PHILOSOPHY
December, 2014
© 2014
Vanessa
All rights reserved.
In loving memory of my grandmother Kanti Devi (1937 -1997) and my grandfather
Veer Pal Singh (1933-2011)
Table of Contents
General Introduction 1
Chapter 1. A psychophysiological investigation of the role of emotion in moral
judgments of real-life situations 17
Abstract 17
Introduction 18
Methods 19
Results and Discussion 23
References 29
Chapter 2. fMRI study of the role of emotion and empathy in moral judgments
of real life situations 32
Abstract 32
Introduction 33
Methods 36
Results 42
Table 2.1 coordinates of peak voxels activated during conditions of judging
(a) happy versus sad, (b) happy versus neutral, and (c) neutral vs. sad
transgressors 43
Discussion 48
References 54
Chapter 3. Neural network connectivity and inter-network interactions during moral
judgments of real-life situations 60
Abstract 60
Introduction 61
Methods 64
Results 68
Discussion 70
References 75
Chapter 4. Role of empathic patterns and emotion in moral judgments of real-life
situations 80
Abstract 80
Introduction 80
Methods 82
Results 89
Discussion 90
References 93
Chapter 5. Behavioral indicators of cognitive processing during moral judgments of
real-life situations 96
Abstract 96
Introduction 97
Methods 98
Results 103
Discussion 103
References 105
General Conclusion 107
References 111
List of Figures
Figure 1-1. Bar graph depicting the average ratings for moral inappropriateness across
all three video types of happy, sad and neutral transgressors…………………26
Figure 1-2. Bar graph depicting the average ratings for guilt displayed by the
transgressor across all three video types of happy, sad and neutral
transgressors………………………………………………………………..27
Figure 1-3. Bar graph depicting the average change in heart rate 1-3s following onset
of transgression, across all three video types of happy, sad and neutral
transgressors………………………………………………………………28
Figure 1-4. Line graph depicting the average change in skin conductance following
onset of transgression, across all three video types of happy, sad and neutral
transgressors……………………………………………………………….29
Figure 2-1. Depiction of neural regions that showed signal change corresponding to
contrasts of judging (a) happy vs. sad transgressors, (b) happy vs. neutral
transgressors, and (c) neutral vs. sad transgressors………………………..46
Figure 2-2. Representative images of neural regions whose activity covaried with (a)
more severe judgment of the happy transgressor and (b) less severe
judgment of the sad transgressor…………………………………………..48
Figure 2-3. Bar graph depicting percent change in signal in the vmPFC ROI (inset)
across all three video types of happy, sad and neutral transgressors………49
Figure 2-4. Line graph depicting time course of vmPFC activation associated with all
three video types of happy, sad and neutral transgressors ………………..50
Figure 3-1. Representative images of independent components corresponding to the
default mode and salience networks…………………………………….74
Figure 3-2. Representative image of the neural regions whose functional connectivity
to the overall DMN component correlated with less severe rating of sad
transgressors……………………………………………………………..75
Figure 3-3. Representative image of the neural regions whose functional connectivity
to the overall DMN component correlated with more severe rating of
happy transgressors……………………………………………………..75
Figure 4-1. Representative image of the ventral anterior insula region of interest…92
Acknowledgments
I am deeply indebted to my advisor Dr. Antonio Damasio for his fundamental role
in my doctoral work. Dr. Damasio introduced me to the wonderful world of socio-
affective neuroscience. He provided me with the inspiration and strength to venture into
research on my own and branch out into new research areas. Dr. Damasio gave me the
freedom to undertake research projects that interested me, at the same time contributing
valuable feedback, advice and encouragement.
I would like to thank Dr. Mary Helen Immordino-Yang for providing me with
guidance and assistance and letting me on to new, exciting research projects. She has
taught me how to truly understand my data and ask new questions. I appreciate all her
time and ideas to make my PhD experience productive and stimulating. I am also
thankful for the excellent example she has provided as a successful woman
neuroscientist.
I am very thankful to Hanna Damasio for every bit of guidance, expertise,
encouragement and love she has given me. I am forever grateful to her for being my
pillar of strength and support.
I am particularly thankful to Kaspar Meyer, who played a key role in the initiation
of both: my academic career at the Brain and Creativity Institute and my doctoral project.
I greatly value the close personal rapport that Kaspar and I have forged over the years. I
simply cannot imagine a better teacher.
I would like to give my heartfelt thanks to Jeremy Kagan, Dave O’ Brien, Laura
Cechanowicz and all the actors from the USC Film School who helped make the video
clips that were used as stimuli in the experiment.
I would also like to thank my dissertation committee, Drs. Jesse Graham and
Stephen Finlay. I am grateful to Dr. Margaret Gatz and Dr. Steve Lopez for supporting
me throughout my PhD years. A heartfelt thanks to all my mentors so far: Drs. Josef
Parvizi, Niels Birbaumer, Nikos Logothetis. Ute Strehl, Peter Schlottke, Sandi Carmen,
Nouchine Hadjikhani, Randy Gollub, John and Sue Gabrieli, Roberto and Gina Prado.
I am forever indebted to my friends and colleagues at the Brain and Creativity
Institute and the Psychology Department for their inspiring conversations and
comradeship, especially Fei Yang, Helder Filipe, Glenn Fox and Eustace Hsu.
I am grateful to everyone who has helped me in data collection and analyses
including Dr. Jonas Kaplan for countless helpful suggestions and comments, and
Jiancheng Zhuang for MRI data collection. I would also like to thank all the research
assistants who helped with behavioral data collection and analyses: Sridevi Korand,
Stephanie Landicho, Sana Yaklur and Jordan M Seeley. I also want to thank the
administrative staff: Susan Lynch, Pamela McNeff, Denise Nakamura, Twyla Ponton,
Irene Takaragawa and Sandy Medearis.
I acknowledge with tremendous and deep thanks, my family: my granddad
“Daddy,” my late grandmother “Chachi,” my parents Dr. Shashi Singh and Mr.
Y.P.Singh, my brothers Shashank, Utkarsh, Kevlar, my sister Krissana, my uncles and
aunts Drs. Vijaya and Ajay K Singh, and Dr. Rajeev and Mrs. Nisha Singh. My family
has given me unconditional love and support and has always encouraged me to pursue
my dreams! I love them with all my heart.
Very special thanks to my friends Samantha Wood, Neha Agarwal and Ridhima
Agarwal, my Californian family: John and Lesley Heaton, Nani, Piassa, Kim and Holly.
Abstract
Recent work in psychology has demonstrated an influence of emotions, such as
compassion, anger and disgust on the level of punishment levied on a moral transgressor
(DeSteno, Bartlett, Baumann, Williams, and Dickens, 2006; Ugazio, Lamm, and Singer,
2012). Neuroimaging studies too have investigated moral reasoning, and suggest that
moral judgment engages brain structures similar to those involved in emotion processing
and perspective taking. To better understand the influence of emotion and empathy on
moral judgment, I created video clips that depict transgressors engaging in everyday,
real-life immoral acts and depending on the outcome of the act, transgressors would
display a happy, sad or no emotion. I investigated participants’ psychophysiological
responses (heart rate and skin conductance), neural activity patterns and natural behavior
as they made judgments of the transgressors’ actions.
My dissertation is comprised of five chapters. In Chapter 1, I demonstrated that
changes in participants’ psychophysiological responses i.e. heart rate and skin
conductance was related to the influence of the transgressor’s emotion display on the
participants’ moral judgment.
In Chapters 2 and 3, I elucidate the neural basis of moral judgment of everyday
life situations and how emotion displayed by the transgressor influences activity in those
brain regions. I also probed network connectivity, specifically the interaction between the
default mode network of the brain with other brain regions during moral judgment, and
the relationship between inter-network connectivity of the default mode and salience
networks of the brain and moral judgment. I found that subjects whose moral judgments
were influenced by the transgressors’ emotion display showed stronger connectivity at
rest between the default mode network and brain regions engaged in emotion processing
and self-related processing.
Finally, I briefly discuss the finding of how participants’ trait empathy correlate to
differences in the influence of emotion on moral judgment. I end with a brief discussion
of the correlation between the participants’ natural, spontaneous behavior and their moral
judgment.
1
General Introduction
Background
In recent years, the study of moral judgment has undergone a renaissance
characterized by two marked changes (Haidt, 2007). First, the scientific study of morality
has become broad and interdisciplinary, drawing from all quarters of psychology,
neuroscience, philosophy, biology and anthropology. Second, the increasing role of
emotion in moral judgment research.
Though most recent research on moral judgment is experimental, the issue of
moral nature of man began thousands of years ago in ancient Greece. For a long time,
morality was discussed within the realms of philosophy, theology and jurisprudence and
postulated as an immaterial concept, noncompliant to scientific study. The question of the
nature of man’s moral sense was raised by distinguished philosophers such as Kant, Plato
and Aristotle and David Hume. While Kantian moral philosophy emphasized the role of
reason in moral judgment, sentimentalists such as Francis Hutcheson (1694-1746) and
later David Hume asserted that moral concepts spring from sentiments. While Kantian
moral psychology emphasizes the role of reason in moral judgment, sentimentalists such
as Hume emphasized moral sentiments such as feelings of approval or disapproval, praise
or blame, esteem or contempt.
The scientific approach to the question of morality emerged due to convergence
of different lines of research. One of the first attempts to empirically approach the
question of moral behavior was made, indirectly, by Italian physician Cesare Lombroso’s
study of sociopathy. Lombroso measured the form and size of the several criminals’
heads and concluded that the somatic traits of these individuals were similar to primitive
2
men and that their antisocial tendencies were present at birth. His anthropological theory
of delinquency was published as “L’uomo Delinquente” (“The delinquent man”) in 1876.
Around the same time in 1806-1812, Philippe Pinel and Benajmin Rush independently
provided descriptions of behaviors that were subsequently labeled as Psychopathic. Pinel
and Rush reported cases of individuals who showed deviant behaviors without a sense of
regret or guilt for the negative consequences of their actions, and emphasized the
antisocial and irresponsible presentation of these individuals (Pinel, 1809; Rush, 1812).
In the fifth edition (1896) of his “Psychiatrie:Ein Lehrbuch” (“Textbook of Psychiatry”),
Emil Kraepelin, for the first time defined a condition of “psychopathic state”;
constitutional disorders that exposed the affected individuals to the development of
personality disorders throughout their lives.
It was however, the famous case of railroad construction worker Phineas Gage,
that opened unexpected horizons and fostered new questions in the neurological basis of
personality and moral behavior. In 1848, at the age of 25 years, Gage had a tragic
accident at the work site when a charge exploded in front of his face, pushing a metal bar
through the skull and the frontal portion of his brain. After months of convalescence,
Gage completely recovered physically, with normal hearing, speaking abilities, body
movements, and normal vision in the right eye. However, what Gage did not recover
from the accident was his character. Medical reports described him as “insolent, vulgar,
intolerant of limitations in contrast to his own desires and incapable of working through
future plans.” Gage’s story is critical as it evidenced that brain damage could impair
regulation of personality and behavior and ethical rules and norms. Behaving morally and
making sound moral judgments requires not only a knowledge of social rules and
3
strategies but also the integrity of specific cerebral systems (Marazziti, Baroni, Landi,
Ceresoli, and Dell’Osso, 2013; Damasio, 1994; Macmillan, 2000; Ratiu, Talos, Haker,
Liberman, and Everett, 2004).
Defining morality
“Moral” is derived from the Latin word “moralis” and originally referred to the
consensus of manners and customs within a social group, or an inclination to behave in a
certain way and not in certain other ways. While philosophical theories aim at identifying
universal principles that should guide human conduct, the focus of psychology is the
rules that are followed by all ‘normal’ persons regardless of their cultural theories of what
is right and wrong.
Moral psychology
As it first started out, developmental psychology seemed to have the sole rights
over the study of moral judgment. Lawrence Kohlberg’s theory of cognitive development
(1969) was the focus of psychology research. Kohlberg questioned boys and young men
about their thought processes when they were faced with a moral dilemma, producing six
stages of moral development that children progress through in their capacity to reason
morally. This ‘rationalist’ approach to moral psychology gained a large following in the
20
th
century under the stewardship of Kohlberg. Nonetheless, by stating that women and
men might have different moral sensibilities, Carol Gilligan (1993) not only attacked
Kohlberg’s presumed gender neutral theories, but also introduced the role of gender
differences in human moral development.
4
However, there were two lines of work that presented a critical break in the form
of cross-disciplinary research and provided the platform for introducing the intuitionist
perspective. One was Antonio Damasio’s Descartes Error (1994), which documented,
using neuroimaging, that rationality in moral reasoning depended crucially on the proper
functioning of emotional circuits in the prefrontal cortex of the brain. The second was
Frans de Waal’s Good Natured (1996), which showed that building blocks of human
morality are also found in other apes and are products of natural selection in the highly
social primate lineage.
If natural selection preserves the building blocks of moral reasoning, which may
be present before language and if emotion plays an important role in driving moral
reasoning, then why should we focus on verbal reasons that people give to explain their
moral judgments of hypothetical moral dilemmas?
Moral neuroscience
Damasio’s work on patients with frontal lobe damage provided evidence that
specific brain regions may be crucial for moral judgment (Damaiso, Grabowski, Frank,
Galaburda and Damasio, 1994). Along with his colleagues, he described moral behavior
deficits in a patient with damage to ventromedial prefrontal cortex (vmPFC) acquired in
adulthood. It was later shown that vmPFC lesions acquired at an early age led to deficits
in moral behavior and judgment indicating that early vmPFC damage could impair moral
development. The advent of neuroimaging not only confirmed the findings of
neuropsychology, but also shed new light on the study of moral judgment. Though still
fraught with the conflicting question of rationality versus emotional intuition, scientists
5
were now able to explore the neural correlates of judging moral dilemmas. fMRI
(functional Magnetic Resonance Imaging) studies in healthy individuals involved
judgment of visually presented moral dilemmas. These studies helped define the
neuroanatomy of moral judgment and found consistent engagement of brain regions
including the vmPFC and adjacent orbitofrontal cortex (OFC), the dorsolateral prefrontal
cortex (dlPFC), anterior cingulate cortex (ACC), posteromedial cortex (PMC), anterior
insula, amygdala, superior temporal sulcus (STS), temporoparietal junction (TPJ)
(Mendez and Lim, 2004). The vmPFC and the OFC are involved in anticipating future
outcomes, theory of mind, empathy and mediating socially aversive responses, inhibiting
impulsive responses respectively (Mendez, Lauterbach, and Sampson, 2008; Mendez,
Chen, Shapira, and Miller, 2005). Cognitive control processes afforded by the dlPFC and
the ACC have been attributed to overriding emotional responses (which are attributed to
the anterior insula, PMC and the STS) during moral judgment thereby producing
utilitarian responses to moral dilemmas. Located in the antero-medial temporal lobes, the
amygdala is involved in modulating responses to situations perceived as frightening or
threatening. Both the STS and the TPJ have been shown to be engaged in social
perception, belief and intention attribution (Saxe and Kanwisher, 2003; Saxe and Wexler,
2005). It became evident through these cognitive subtraction fMRI studies of moral
judgment that brain regions involved in emotion processing (vmPFC, TPJ, STS, PMC)
were also activated during moral judgment. These findings turned the traditional
rationalist picture on its head. The familiar narrative arc in rationalist moral psychology
which emphasized reason as the basis of moral judgment was now confronted with
6
converging evidence from psychology, lesion and neuroscience studies supporting the
role of emotion in moral judgment.
There is now an increasing recognition of the central role of emotion in moral
behavior rather than being an extraneous process that interferes with moral reasoning.
The first approach to the questions of the role of emotion processing in moral judgment
comprised testing individuals with deficits in emotional processing. Studies of patients
with early onset vmPFC lesions, and of patients with frontotemporal dementia (FTD)
which involves deterioration of prefrontal and anterior temporal brain areas have shown
impaired moral judgment making in these groups (Anderson, Bechara, Damasio, Tranel,
and Damasio, 1999; Damasio, 1995; Koenigs et al., 2007). Like FTD patients, patients
with lesions in the vmPFC exhibit blunted affect, diminished empathy, but unlike FTD
patients, vmPFC patients show a broader intellectual function. Research with adult and
developmental psychopaths has also provided support for the association between
emotional impairment and defects in moral judgment. Psychopathy is characterized by
pronounced emotional impairment including reduced empathy and guilt. Both adult and
developmental psychopaths have been found to have difficulty distinguishing between
unambiguous moral transgressions (e.g. hitting someone) and unambiguous conventional
transgressions (e.g. talking out of turn) along the dimensions of permissibility,
seriousness and authority contingence. A coarse summation of the clinical findings, it
seems is that individuals who exhibit abnormal emotion processing also exhibit
systematically abnormal moral judgment. The findings of neuropsychology cannot be
simply extended to healthy, ‘normal’ human beings, however, advances in neuroimaging,
allow us to bolster these findings.
7
Empirical research has tended to focus on (a) whether moral judgments stem from
intuitions or from conscious reasoning, and (b) which psychological processes are
involved in moral intuitions (Cushman, Young, and Hauser, 2006). It has been suggested
that moral judgments are critically influenced by intuitions and that these intuitions are of
an emotional nature (Cushman et al., 2006; Haidt, 2001; Schnall, Haidt, Clore, and
Jordan, 2008; Wheatley and Haidt, 2005). To test his idea that moral judgments result
mainly from ‘gut feelings’ Haidt and colleagues (Schnall et al., 2008; Wheatley and
Haidt, 2005) developed a series of moral vignettes describing moral transgressions
strongly connected to the feeling of disgust. For instance, a vignette describing two
siblings who have sexual intercourse. As per his hypothesis, Haidt showed that these
vignettes induced feelings of disgust in the participants and influenced their judgment of
whether the protagonists’ intention was moral permissible. An alternative theory agrees
with the role of intuitions in moral judgment but asserts that rather than emotions,
intuitions result from the psychological mechanism of ‘universal moral grammar’, which
focuses on the protagonists’ intentions in moral judgments (Hauser, 2006; Huebner,
Dwyer, and Hauser, 2009; Mikhail, 2007). More importantly, ‘moral grammar’ is
independent of both emotional and cognitive mechanisms that are activated only after a
moral judgment. Accordingly, emotions and reasoning play no causal role in moral
judgment (Hauser, 2006; Huebner et al., 2009). A third point of view is provided by
Greene and colleagues (Greene and Haidt, 2002; Greene, Nystrom, Engell, Darley, and
Cohen, 2004; Greene, Sommerville, Nystrom, Darley, and Cohen, 2001) who purport a
conciliatory position to the above two. Greene and colleagues articulated a dual process
model, which posits that we have two different brain circuitries, one a cognitive system
8
and second an automatic emotion processing system, that work in unison but often in
competition to shape moral judgments. They presented their subjects with more
emotional or ‘personal’ moral scenarios and less emotional or ‘impersonal’ moral
scenarios and showed that activation of certain brain regions such as the vmPFC was
associated only with the personal scenarios. Greene and colleagues further tested the
hypothesis that emotion is associated with not only particular moral scenarios but with
particular moral judgments, specifically ‘non-utilitarian’ judgments that are based on
factors other than the sum consequences of the action in question. They have garnered
functional neuroimaging support for the dual process model, with activity in the
dorsolateral prefrontal cortex, posterior cingulate cortex, and inferior parietal lobe
thought to indicate cognitive control processes during difficult, personal moral decision
making. In contrast to Greene’s view, Moll and colleagues propose what they call ‘moral
emotions’ neither compete with rational processes during moral judgments, nor do they
result from moral judgment. Moral emotions, are defined by Moll and colleagues (Moll,
de Oliveira-Souza, Bramati, and Grafman, 2002; Moll, De Oliveira-Souza, and Zahn,
2008), as being linked to the interest or welfare either of society as a whole or at least of
persons other than the agent (Haidt, Koller, and Dias, 1993). In the taxonomy of moral
emotions one finds for instance, guilt, pride, shame, embarrassment, contempt and awe.
Moll and colleagues (2002) suggest that moral emotions help guide moral judgments by
attaching value to whichever behavioral options are contemplated while making a moral
judgment.
9
Within the limits of current neuroimaging techniques, the observed patterns of
activation in these studies can be taken as evidence for an association between emotion
processing and moral judgment.
Recent studies in psychology have delved further into the role of specific emotion
types in modulating moral judgment. DeSteno and colleagues (Condon and DeSteno,
2011; Valdesolo and DeSteno, 2006) examined the effect of compassion felt towards one
person on reducing the punishment directed towards another. They found that participants
who were induced to feel compassion towards a separate individual, reduced the amount
of punishment levied on the cheater, even though the cheater intended to cheat. Similarly,
Ugazio and colleagues (Ugazio, Lamm, and Singer, 2012) demonstrated that induction of
emotions associated with approach motivation such as anger would make moral
transgressions more permissible, while disgust associated with withdrawal motivation
would make moral transgression less permissible.
While, scientific data seems to establish the role of emotion in moral judgment,
the studies thus far seem to have some limitations:
First, most of these studies have addressed primarily broad questions regarding
the role of emotion in moral judgment leaving significant questions unanswered. For one,
the data so far fails to demonstrate that emotion triggers a change in moral judgments.
For example, do emotions modify the severity of our moral judgments?
Second, in psychology most study designs present the emotional stimuli before
the moral judgment task, for example by having subjects watch a humorous movie,
enabling emotion to influence the interpretation of the question. Further, such emotion
induction is phenomenologically different from an original emotion experience.
10
Third, moral and emotion processes are strongly influenced by the contextual
factors and abstract moral dilemmas cannot serve as a proxy for moral judgment in real
life situations.
Lastly, fMRI and behavioral studies are only correlation data that do not allow
for the inference of causality. Further, given the limitations of spatial and temporal
resolution of various methods, research combining multiple methods is warranted.
With these unanswered questions and research gaps in mind, we designed the
present study to investigate the role of empathy and emotion in the moral judgment of
real-life situations. We designed video clips that depict transgressions in real-life context
such as stealing someone’s bag or opening someone else’s private mail package. Video
clips were made amenable to manipulation such that across similar clips, the transgressor
may display a happy emotion if the moral transgression benefitted him/her (e.g. opening
your professor’s mail and finding out he wrote you a good recommendation), a sad
emotion if the moral transgression did not benefit him/her (e.g. opening your professor’s
mail and finding out he wrote you a bad recommendation), or no emotion at all. We
employed multi-method data collection and analysis including behavioral,
psychophysiological and neural data to answer the following questions:
1. How does emotion display by the transgressor influence the observer’s moral
judgment?
2. How does the observer’s empathic tendency influence his/her moral judgment?
3. How are psychophysiological measures of heart rate and skin conductance
responses related to the observer’s moral judgment of the transgressor?
11
4. What are the neural regions involved in moral judgment of real life situations?
5. How is the activity of these neural regions influenced by the emotion display of
the moral transgressor?
6. Is there an association between functional connectivity between brain regions and
moral judgment of the transgressors?
12
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cognition. Annals of the New York Academy of Sciences, 1124(1), 161-180.
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Gage, digitally remastered. Journal of Neurotrauma, 21(5), 637-643.
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the temporo-parietal junction in ‘theory of mind’. NeuroImage, 19(4), 1835–42.
Saxe, R. and Wexler, A. (2005). Making sense of another mind: the role of the right
temporo-parietal junction. Neuropsychologia, 43(10), 1391–9.
Schnall, S., Haidt, J., Clore, G. L., and Jordan, A. H. (2008). Disgust as embodied moral
judgment. Personality and Social Psychology Bulletin.
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17
Chapter 1: A psychophysiological investigation of the role of emotion in moral
judgments of real-life situations.
Abstract
It is well established that the process of decision making is influenced by emotion
and feelings (Bechara and Damasio, 2005; Damasio, 1994, 2000), and it has been shown
that moral judgments are also influenced by emotions. For instance, participants in a
heightened emotional state are harsher in their moral judgments of a transgressor
(Schnall, Haidt, Clore and Jordan, 2008; Wheatley and Haidt, 2005). While physiological
changes associated with emotion during decision making have also been investigated
(Bechara et al., 1996; Anderson et al., 1999), none of the existing studies has directly
measured emotional responses in healthy participants during the type of moral judgments
encountered in everyday life situations. Here we attempted to establish a direct
psychophysiological link between emotion and moral judgment in realistic everyday
scenarios. Specifically, we recorded heart rate (HR) and skin conductance responses
(SCR) in healthy participants while they made judgments about moral transgressors who
displayed a happy or sad emotion at the outcome of their transgression. As hypothesized,
happy looking transgressors, who benefitted from the immoral act, were judged most
severely and thought to show the least amount of guilt, they also elicited the greatest HR
deceleration and SCR change. These responses were not seen when the transgressors
displayed a sad emotion or no emotion at all. Our findings provide direct
psychophysiological support for the role of emotion in moral judgment.
18
Introduction
The somatic marker hypothesis (Damasio, 1994) postulates that the process of
decision making is influenced by signals that arise in the bioregulatory processes, somatic
markers, including those that express themselves in emotions and feelings. There is
evidence that physiological changes related to emotion play a role in decision making,
such as, e.g. in the gambling task (Anderson et al., 1996; Bechara et al., 1999). Recent
work in moral psychology also emphasizes the role of emotion in moral judgment (Haidt,
2001). Using moral dilemmas, it has been shown that “personal” moral dilemmas (such
as pushing a man off the bridge to save others) engage brain regions associated with
emotion processing, unlike “impersonal” moral dilemmas that do not engage emotional
responses in the participants (Greene et al., 2001 and Greene, 2004). Perhaps the most
direct evidence for the role of emotion in moral judgment comes from lesion studies that
demonstrate impaired moral judgment in patients with damage to the ventromedial
prefrontal cortex (vmPFC), a brain region involved in coordinating affective responses
during the decision-making process (Damasio, 1996). More recently, Moretto and
colleagues (2010) showed that vmPFC patients approve of more personal moral
violations than healthy controls, and found, importantly that unlike controls, vmPFC
patients failed to generate SCRs before endorsing personal moral violations. Their study,
however, as is the case in most published studies, employed moral dilemmas that require
participants to imagine extreme and hypothetical situations which may not be realistic
and relevant to them. Others have induced emotional responses by asking participants to
relive a previous emotional episode (Wagner et al., 2011) or watch a humorous movie,
techniques which do not resemble the way emotions are normally evoked during moral
19
judgment. In this study we opted, instead, for measuring, with direct
psychophysiological probes, the emotions induced by moral judgment of real-life
situations in healthy subjects.
We elicited emotions in the observer by manipulating the outcome of the
transgression and the emotion displayed by the transgressor. The transgressor displayed a
happy emotion when the immoral act was personally beneficial, and a sad emotion when
the immoral act was not beneficial; or, on the other hand, no emotion at all. We then
investigated how the emotions displayed by the transgressor influenced the
psychophysiological response of the observer, during the judgment of the transgressor’s
action. Because making moral judgments requires us to engage in perspective taking and
simulate the experience of another person, we hypothesized that display of a happy
emotion by the transgressor would elicit more emotional arousal in the observer than the
display of a sad emotion or none at all, and that this difference would be translated in the
observers’ severity of the moral judgment and be correlated with differential changes in
their heart rate and skin conductance responses.
Methods
Twenty seven right handed adults (18 females) between the age of 18 and 38
years (average age of 20.7 years), born and raised in the USA, with English as their first
language, participated in the study. We used a variety of video clips that depict everyday
situations with (i) moral transgressions without any display of emotion (e.g. a student
opening her professor’s personal mail); (ii) moral transgressions with positive emotional
display (e.g. a student reading the letter in her professor’s mailbox and showing a happy
20
expression after finding out that her professor wrote a good recommendation); (iii) moral
transgressions with negative emotional display (e.g. a student reading the letter in her
professor’s mailbox and showing a sad expression after finding out that her professor
wrote a poor recommendation). The video clips were produced with the help of the USC
Film School and were shot from a security camera perspective. There were always two
persons depicted in the clips and the observers were told they were students on campus
unless indicated otherwise by the text accompanying the video. Each video clip was
shown on a monitor, was 23s long and was followed by a 13s fixation cross on a black
screen, without any sound. The videos were balanced and presented in pseudo-random
order so that no observer would see the same transgressor twice. Each observer saw 4
video clips from each of the three categories of emotional expressions by the
transgressors: happy, sad and no emotion. Psychophysiological responses, skin
conductance response (SCR) and heart rate response (HR), were recorded throughout the
experiment while twenty seven participants viewed the clips and responded immediately
thereafter, to the question ‘Did s/he do something wrong?’. The answer consisted of a
bottom press on a scale from 1 (not wrong) to 4 (very wrong). Participants used their
right hand to respond to the question by pressing the number key on the computer
keyboard.
Two analyses were performed. The first analysis explored how a participant’s
moral judgment was influenced by the moral transgressor’s display of positive or
negative emotion following the immoral act. For example, would a participant make a
milder judgment if the transgressor was visibly upset after reading a letter he was not
supposed to read? Specifically, we examined the association of the moral transgressor’s
21
display of emotion with the severity of the observers’ judgment. We also wanted to
establish to which degree the participant’s moral judgment would be influenced by their
interpretation of the amount of guilt displayed by the transgressor. To quantify this
aspect, we assessed the participant’s ratings of the transgressor’s display of guilt by
having them respond to, “How guilty does s/he look?” on a scale of 1 (not guilty) to 4
(very guilty). We performed an ANOVA and found marginal differences in guilt rating
across the 3 emotion conditions (F (2,52)= 2.939, p =0.062).
In the second analysis, we wished to understand how the participant’s
psychophysiological response, specifically heart rate and skin conductance responses,
were influenced by the transgressor’s display of emotion.
Physiological measures
ECG data was collected by placing removable electrodes: one negative on the left
wrist, positive on the right ankle, and SCR by pasting two electrodes on the palm of the
left hand. ECG recording was sampled at 4000Hz and SCR at 1000HZ during the moral
judgment task. Acknowledge 9.32 (Biopac Systems Inc.) was used to preprocess the ECG
recordings and to identify the R peaks of the QRS complex. The resulting intervals
between the R peaks were plotted and visually inspected for artifacts. Misidentified R
peaks were manually corrected. RR interval series were then uniformly re-sampled at 4
Hz using cubic spline interpolation (Kubios HRV; kubios.uku.fi/). Uniformly sampled
RR interval series were transformed into heart rate series (in beats per minute, bpm).
Across all the videos, the onset of the moral transgression varied between 12-14sec after
video onset. To account for this difference, we individually aligned each participant’s HR
22
response at the onset of the transgression in each video. The aligned HR response was
then normalized to the whole previous video preceding the transgression and averaged
across participants for each of the three video types: happy, sad and neutral transgressor.
To account for individual differences in HRV, we computed individually for each
participant and each video type, the minimum point in the HR change in a window of 1-
3s following the onset of the transgression. Finally, we averaged these values within each
participant, by condition and then performed significance tests across the values.
Repeated measures ANOVA revealed a trend towards significant difference between the
three conditions (F (2,108)=2.252, p=0.110). A paired t-test revealed differences in HR
deceleration between watching the neutral and the happy transgressors (t (54)=2.001,
p=0.050), but no difference between the neutral and the sad transgressor (t (58)=1.494,
p=0.141).
SCR data were pre-processed in Acknowledge 9.32 (Biopac Systems Inc.) by first
rescaling the raw signal from volts to micromhos. The converted signal was then low
pass filtered using the Blackmann 61dB filter at a fixed frequency of 1Hz, and resampled
at 125Hz. The pre-processed data were then exported to Matlab for further analysis. As
for HR, for each participant, we individually aligned each participant’s SCR to the onset
of the transgression of each video. We then normalized the aligned signal to the video
preceding the onset of the transgression. Given the slow onset and rise time of an SCR
response and to capture a specific response, we decided to look at the time window of 20
to 24s, which corresponds to the time the participant forms and reports his/her judgment
of the moral transgression. In this window, we averaged the SCR for each video type and
conducted repeated measures ANOVA which revealed a significant difference between
23
the three video types of happy, sad and neutral (F (2,750)=843.637, p<0.001). Further, t
tests revealed significant differences between neutral and happy transgressors (t
(375)=4.708,p<0.001), neutral and sad transgressors (t (375)=37.938, p<0.001), and
happy and sad transgressors (t (375)=51.126, p<0.001).
Discussion
Our study had two aims: first, to establish whether emotion display by the
transgressor would influence the participant’s moral judgment in terms of its severity;
and how the displayed emotion would affect the amount of guilt attributed to the
transgressor. Second, to investigate the profile of the participant’s psychophysiological
response as a consequence of the emotion displayed by the transgressor.
In response to our first aim, we found that the display of positive emotion by
transgressors who benefitted from the immoral act was judged more severely by the
participants as compared to display of a sad or no emotion (F (2,50)=4.388, p=0.018;
24
Figure1-1).
Figure 1-1: Average ratings for moral inappropriateness. Average ratings of the
transgression given by participants across all videos in response to the question “Did s/he
do something wrong?” on a scale of 1(not wrong)-4(very wrong). The videos where the
transgressor benefitted from the transgression and displayed a happy emotion were
judged significantly (F (2,50)=4.388, p=0.018) more severely than the videos where the
transgressor displayed a sad or no/neutral emotion.
Also, participants rated the transgressor displaying a sad emotion as displaying
more guilt as compared to the transgressor displaying a happy emotion (t (26)=-2.195,
25
p=0.037;Figure1-2).
Figure 1-2: Average ratings of the perceived guilt displayed by the transgressor given by
participants across all videos in response to the question “How guilty do you think s/he
looks?” on a scale of 1(not guilty)-4(very guilty). Transgressors who did not benefit from
the transgression and displayed a sad emotion were judged as displaying significantly
more guilt than the videos where the transgressor displayed a happy or no emotion (F
(2,52)= 2.939, p =0.062).
Consistent with previous findings, our results of less severe ratings of the sad
transgressor and the more severe ratings of the happy transgressor, suggest that
attributions of guilt are positively associated with expression of sympathy (Carlo et al.,
2012), and that guilt is negatively associated with anger. Our data also suggest an
interplay between the display of emotion by the transgressor and the attribution of guilt
by the participant. Corroborating these behavioral findings, our psychophysiological data
revealed that participants showed a trend towards greater heart rate deceleration when
26
viewing the transgressor display an emotion (happy or sad), than viewing the neutral
transgressor (happy>neut, p=0.050; sad>neut, p=0.141; Figure 4). Heart rate deceleration,
a few seconds following stimulus onset, has been associated with allocation of attentional
resources necessary to stimulus perception (Lacey and Lacey, 1970).
Figure 1-3: Individuals showed a trend towards greater heart rate (HR) deceleration when
watching the transgressor display an emotion (happy or sad) as compared to the neutral
transgressors. HR deceleration in the 1-3s following the onset of the transgression across
all videos. Notice that participants’ HR in response to the videos where the transgressor
displayed a happy emotion (red) shows more deceleration as compared to when the
transgressor displayed a sad emotion (green), or no emotion (blue).
In addition, we found that participants showed significantly greater change in skin
conductance when making judgments about the happy transgressor than about the sad or
27
neutral transgressors (t(375)=51.126, p<0.001; Figure1-4).
Figure 1-4. Participants showed significantly greater increase in skin conductance when
judging the happy transgressor as compared to the sad or neutral transgressors (p<0.001).
Change in skin conductance rate (SCR) is aligned to the onset of the transgression
(corresponding to 0sec) across all videos. Notice that participants’ increase in SCR
(window shaded in blue from 20-24s) while making the judgment for happy transgressor
(red) as compared to when the transgressor displayed a sad emotion (green), or no
emotion (blue).
It is known that both heart rate and skin conductance are sensitive measures of
emotion processing (Dawson et al., 2007, Boucsein, 1992, 1999) and can serve as an
index of bodily states of arousal that guide social behavior and decision making (Bechara
and Damasio, 2005; Damasio, 1994,1996). Changes in heart rate and skin conductance
may serve as affective signals elicited in response to the transgressor’s display of emotion
and thus facilitate the participant’s decision making process. The behavioral findings
related to the most severe rating, quickest response and attribution of least guilt felt, were
28
seen in the participant’s response to the happy transgressor. These psychophysiological
responses suggest that the participant’s judgment of the happy transgressor was the
strongest. Perhaps, this reflects our aversion to morally reproachable situations (Knobe,
2003a) that are more likely to be considered intentional. Evolutionarily, we may be more
aversive in our response to free-riders or cheaters. In support of this interpretation, a large
majority of the available literature on perception shows lower heart rate when unpleasant
stimuli are presented compared to the presentation of pleasant stimuli (Hare et al., 1970;
Libby et al., 1973; Winton et al., 1984; Greenwald et al., 1989; Bradley et al., 1990; Lang
et al., 1993, 1998; Bradley and Lang, 2000; Anttonen and Surakka, 2005; Codispoti and
De Cesarei, 2007; Sokhadze, 2007). In contrast, the perception of guilt is positively
associated with sympathy (Carlos, 2006) and it is likely that the judgment of the sad
transgressor elicited an inclination to empathize, reflected by the fact that this response
took longer.
Our findings positively indicate the involvement of emotion in guiding moral
judgment even in real-life situations. Manipulation of the transgressor’s display of
emotion was shown to influence participants’ psychophysiological responses during
moral judgment. Given these findings, it could be suggested that emotion display by the
transgressor has the ability to evoke empathy in the participant. This empathy may act as
a force to reduce the severity of judgment of the moral transgression, and suggests that
the capacity for empathy depends, among other factors on the affective state displayed by
the transgressor.
29
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Chapter 2: fMRI study of the role of emotion and empathy in moral judgments of
real-life situations
Abstract
Central to the empirical investigation of moral judgment has been the influence of
emotion on our decisions of right and wrong. Several neuroimaging studies have revealed
an association between processing morally relevant stimuli and activity in brain regions
involved in empathy and emotional responsiveness, in particular the ventromedial
prefrontal cortex (vmPFC; Young and Koenigs, 2007). The aim of this study was to test
hypotheses about the recruitment of brain regions involved in emotion processing,
perspective taking and self-related processing in moral judgment of real-life situations,
and how the activity in these regions was differentially influenced by the transgressor’s
emotion display. In an fMRI experiment, participants viewed video clips depicting moral
transgressions in everyday life scenarios. To investigate the influence of emotion on
moral judgment, we showed transgressors portraying a happy or sad emotion depending
on the result of their transgression: happy if the result was positive for them or sad if it
was negative, or, on the other hand having a neutral expression. Participants were asked
to pass a moral judgment on each of these possible scenarios. Consistent with previous
work, moral judgments were associated with activity in the anterior insula, middle
cingulate cortex, and parts of the temporal pole involved in emotion processing and
perspective taking (Moll et al., 2005; Greene and Haidt, 2002). However, the study also
revealed a previously undescribed effect regarding the role of emotion on behavioral and
neural correlates of moral judgment. Happy transgressors were judged more severely and
33
the judgment was associated with greater activity in regions involved in emotion
processing and decision making such as the amygdala and orbitofrontal cortex. Also, the
activity within the vmPFC, was correlated with the response time and the severity of the
moral judgment: greater and quicker change in vMPFC activity was associated with
judging the happy transgressor than the sad transgressor or the transgressor with no
emotion display.
Introduction
In both psychology and neuroscience, there is ample support for the claim that
emotion plays an important role in moral judgments. Functional neuroimaging studies of
moral judgment have provided consistent evidence for the involvement of brain regions
implicated in emotion processing. In particular the ventromedial prefrontal cortex
(vmPFC) has been shown to be engaged during processing of morally relevant stimuli
and damage to this region results in striking impairments in emotions including blunted
affect, reduced empathy, and poor regulation of anger and frustration (Kringelbach 2005;
Fellows, 2007; Bechara et al., 2000;Damasio, 1996). Other regions that have been
identified to be involved in moral judgments include (1) the posterior superior temporal
sulcus and temporoparietal junction, which are involved in theory of mind and intention
attribution, (2) the anterior cingulate cortex (ACC) which is suggested to be engaged in
mediating conflict between the emotional and rational components of moral reasoning,
and (3) subcortical nuclei such as the amygdala, hippocampus and basal ganglia involved
in emotion and social processing, self-related processing and processing of moral
conflictual stimuli respectively (Moll et al., 2005; Mendez, 2009).
34
Functional Magnetic Resonance Imaging (fMRI) studies of moral judgment began
with the presentation of pictures and statements that had a moral or a non-moral content
(e.g. war scene versus body lesions). Contrasting brain activation patterns elicited by
these stimuli revealed significant signal change in the vmPFC. An important step in
understanding the role of emotion in moral judgments was undertaken by Greene and
colleagues (2001) who found that complex ‘personal’ (putatively more emotional) moral
dilemmas engaged the vmPFC significantly more than ‘impersonal’ (putatively less
emotional) moral dilemmas.
Another and older approach to investigate the role of emotion in moral judgment
is based on the study of persons with structural brain lesions. Lesion studies allow us to
address the question of causal links between emotion and moral judgment and have
shown that individuals with childhood-onset or focal vmPFC damage demonstrate
impaired moral reasoning (Koenigs et al., 2007,2012; Moretto et al., 2010; Damasio,
1999). In addition, studies of patients with fronto-temporal dementia, which involves
deterioration of the prefrontal and anterior temporal brain regions, have shown that such
patients exhibit blunted emotion, diminished regard for others and also poor moral
judgment. Further evidence comes from the study of psychopathy, a complex personality
disorder characterized by behavioral disturbance such as violence, criminal activity along
with emotional impairment depicted as reduced empathy and guilt (Harenski and Kiehl,
2011). Examination of adult and developmental psychopaths have associated emotional
impairment with deficits in moral judgment and a growing body of evidence associates
psychopathy with structural and functional abnormalities in the vmPFC (Raine and Yang,
2006; Koenig, 2012).
35
While the study of clinical populations emphasizes the ‘causal’ role of emotion in
moral judgment (Young and Koenig, 2007), moral reasoning involves several complex
processes that may not lend themselves easily to generalization. Moreover, as in
functional neuroimaging studies, lesion studies too have relied on the use of hypothetical
and extreme moral dilemmas that cannot serve as a proxy for real-life situations.
Recently, in order to understand the role of emotion in moral reasoning it has
been shown in the psychological literature, for instance that people make harsher moral
judgments when they are in a heightened emotional state (Schnall, Haidt, Clore and
Jordan, 2008; Wheatley and Haidt, 2005). Most of these studies have involved eliciting,
a positive or negative affect in the participants prior to the delivery of judgment, by for
example having them watch a humorous movie (Valdesolo and DeSteno, 2006; Shin et
al., 2000; Wagner et al., 2011), which again does not mimic the way emotions are
normally evoked during moral judgment in everyday life.
Here we decided to elicit different emotions in the participants by manipulating the
transgressor’s emotional display. We varied the outcome of the transgression to either be
beneficial or not to the transgressor. More than simply recognizing the transgressor’s
emotional display, we designed our videos so as to elicit emotion feeling in the
participants. Emotion recognition would occur in response to the stimulus presentation,
involving appraisal and identification of the emotion depicted in the stimulus (Phillips,
Drevets, Rauch and Lane, 2003) and would be followed by the generation of an
emotional feeling in the observer. We expected that during moral judgment, participants
would recognize the transgressor’s emotional display, engage in perspective taking i.e.
sharing and understanding the emotional state of the transgressor, experience an
36
emotional feeling and respond differentially to the transgressors’ display of happy, sad or
no emotion (Singer, Critchley and Preuschoff, 2009). Consequently, we expected that
those participants who scored high on trait empathy (measured using the interpersonal
reactivity index) would be more likely to engage in perspective taking and be influenced
by the transgressor’s emotion display.
In brief, although in the past decade considerable effort has been made to elucidate
the role of emotions in moral judgment, here we specifically set out to test the effect of
emotional display by a transgressor on the participant’s moral judgment, both
behaviorally and neurally. Specifically, we hypothesized:
1. that participants would be more severe in their judgment of the happy
transgressors who benefitted from the immoral act than of the sad transgressors
who did not benefit from the immoral act;
2. that moral judgments would engage the vmPFC, parts of the temporal lobe (such
as the middle temporal gyrus, superior temporal gyrus, temporal pole), ACC,
posteromedial cortex (PMC), insula, hippocampus, and basal ganglia.
3. that the activity of the brain regions found in (2) would be differentially
influenced by the transgressor’s display of happy, sad or no emotion.
Method
Participants
Twenty right handed adults between the age of 18 and 35 years (average age: 24
years; 10 females) were recruited from the USC campus to participate in the study. To
ensure consistency along a cultural dimension, we included only those participants who
37
were born and raised in the USA with English as their first language. Participants were
excluded if they did not pass the MRI safety-screening questionnaire or if they had a
history of neurological or psychiatric disorder. All participants had normal or corrected
vision.
Stimulus preparation and selection
In collaboration with a professional film maker (Jeremy Kagan, Emmy-winning
director and head of the Change Making Media Lab of USC’s School of Cinematic Arts),
we produced a variety of video clips that depict everyday situations with (i) neutral
content (e.g. student reading the letter in his mailbox); (ii) moral transgression without
any emotional content (e.g. student reading the letter in his professor’ mailbox); (iii)
moral transgression with emotional content (both positive and negative valence e.g.
student reading the letter in his professor’s mailbox and getting happy after finding out
that his professor wrote him a good recommendation, or getting sad after finding out that
his professor wrote him a poor recommendation). These video clips were edited to
include a short sentence explaining the situation along with an arrow that pointed at the
transgressor in the video so as to clearly identify him/her to the participant. The length
and duration of the text was controlled for each video clip to comprise on average equally
complex words and to be shown for 7sec. The arrow used throughout was the same (in
size, shape, onset of and 4 sec duration of appearance). The video clips were piloted to
ensure emotional effectiveness, moral content, arousal, and equivalence of visual
properties such as size, frame rate, brightness and contrast.
38
Experimental design and protocol
While inside the scanner, participants were presented with video clips in two
separate fMRI runs. Each video clip lasted 23 s, followed by a 13 s fixation cross on a
black screen, so that the entire duration of each trial was total 36 s. We prevented
repetition of videos belonging to the same category by using a pseudorandom order of
presentation. For each stimulus, participants were instructed to look at the video clip for
its entire duration. In the first functional run, participants were presented with a total of
18 video clips, comprising 9 neutral clips with no moral transgression or expression of
emotion, and 9 clips depicting a moral transgression, with neutral expression. After each
video clip, participants were asked a factual question about the clip such as “ Did she
pick up the phone?” to which the participants answered “yes” or “no” via a button press
on a box placed in their right hand. In the second functional run, participants watched
video clips in which the emotion of the moral transgressor was manipulated. In this run,
participants saw 12 video clips, 4 each belonging to the categories of neutral, happy, and
sad emotion displayed by the transgressor. Following the presentation they answered the
question ‘Did s/he (the protagonist) do something wrong?’ Participants responded by
button press to rate the wrongness of the protagonist’s actions on a scale of 1 to 4, from
“not wrong to “very wrong”.
After scanning, participants were debriefed and presented again with the videos
and asked to respond, on a scale of 1 (not at all) to 4 (very much), to the questions of
“Does s/he look happy?” “Does s/he look sad?” “Does s/he look guilty?” The first two
questions were to establish the recognition of the transgressor’s emotion display while the
third question was meant to quantify the guilt the participants’ thought the transgressor
39
displayed. In addition, participants filled out behavioral questionnaires including the
Interpersonal Reactivity Index (IRI, Davis et al., 1980) and the Moral Foundations
Questionnaire (MFQ, Graham et al., 2009).
fMRI acquisition
A Siemens 3-Tesla MAGNETON TIM Trio scanner with a 12-channel matrix coil
at the Dana and David Dornsife Neuroimaging Centre at the University of Southern
California was used to collect imaging data. Functional scans were acquired using a T2*
weighted Echo Planar (EPI) sequence (TR = 2000ms, TE =30ms, Flip Angle = 90º) with
voxel resolution of 3mm by 3mm by 4.5mm. Thirty-two transverse slices were acquired
to cover the whole brain and brainstem. Anatomical scans were acquired using an
MPRAGE sequence (TI=900ms, TR=2530ms, TE =7ms, Flip Angle = 7º) with an
isotropic voxel resolution of 1mm.
Behavioral data analysis
For the first functional run, we calculated the participants’ accuracy in responding
to the factual question following videos depicting a moral transgression and those
depicting a banal scenario without moral content.
For the second functional run, which comprised videos depicting a transgression but
differing in the presence of emotional content, we calculated across participants, for each
condition (neutral, happy and sad transgressor) the average severity ratings, reaction time,
and ratings for guilt, and utilized ANOVA and t-test to determine any significant
differences across conditions.
40
fMRI image processing
Data were preprocessed and analyzed using FEAT (FMRI Expert Analysis Tool)
Version 5.98, part of FSL (FMRIB’s Software Library, www.fmrib.ox.ac.uk/fsl). After
motion correction, images were temporally high-pass filtered with a cutoff period of 200s
and smoothed using an 8mm Gaussian FWHM algorithm in 3D. The BOLD response was
modeled using a separate explanatory variable (EV) for each stimulus type (moral
transgression and banal for functional run 1, neutral, happy and sad transgressor for
functional run 2). For each stimulus type, the presentation design was convolved with a
gamma function to produce an expected BOLD response. The temporal derivative of this
timecourse was also included in the model for each EV. Data were then fitted to the
model using FSL’s implementation of the general linear model.
Each participant’s statistical data were then warped into a standard space based on
the MNI-152 atlas. We used FLIRT (FMRIB’s Linear Image Registration Tool) to
register the functional data to the atlas space in three stages. First, functional images were
aligned with the high-resolution coplanar T2-weighted image using 6 degrees of freedom
rigid-body warping procedure. Next, the coplanar volume was registered to the T1-
weighted MP-RAGE using 6 degrees of freedom rigid-body warp. Finally, the MP-
RAGE was registered to the standard MNI atlas with 12 degrees of freedom affine
transformation.
Random effects higher-level analysis was carried out using FLAME (FMRIB’s
Local Analysis of Mixed Effects) (Behrens et al., 2003). For our whole-brain analysis,
statistical images were thresholded using clusters determined by Z > 2.3 and a corrected
41
cluster size significance threshold of P = 0.05 (Worsley et al., 1992; Friston et al., 1994;
Forman et al., 1995).
Region Of Interest (ROI) definition and analysis
To identify the region(s) of interest engaged during moral reasoning, we
contrasted the first functional run of moral versus banal, across all participants. We
identified the vmPFC as the region that was consistently activated in each participant in
the moral versus banal contrast. We used the Harvard-Oxford cortical atlas to
anatomically define the vmPFC, which we then constrained by the average activation
across participants in the moral versus banal contrast. To investigate how the activity of
the vmPFC was influenced by the emotion display of the transgressor, we used Featquery
(FSL) to extract, for each participant, parameter estimates corresponding to the
conditions of neutral, happy and sad transgressor in run 2.
In addition we used PEATE software (http://www.jonaskaplan.com/peate/) to
extract event related averages (ERAs) from the vmPFC ROI mask for each participant,
for a duration of 10 s before and 40 s after video onset. The ERA of the signal for each
participant was normalized to signal timecourse 10 s preceding video onest. The ERA
enabled us to investigate the different temporal courses of vmPFC activation for the 3
different conditions.
42
Results
Behavioral results
In the first functional run, participants did not show any significant difference in
their accuracy of response (p=0.3148) or response time (p=0.491) to the moral videos
compared with the banal videos.
For the second set of videos, we found that participants were most severe in their
judgment of the happy transgressor (F(2, 38)=4.292,p=0.021). Participants rated the
happy transgressor as showing the least amount of guilt (F(2,38)=4.672,p=0.015), and
they took the least amount of time in judging the happy transgressor
(F(2,38)=8.975,p=0.001).
BOLD results by condition
We found that judging the transgressors displaying a happy emotion compared to
judging the sad transgressors engaged cortical regions of the middle cingulate cortex
(midCC), vmPFC, supramarginal gyrus (SMG) and the primary somatosensory cortex
(SSI). Compared to the neutral transgressor, judging the happy transgressor engaged
more the frontal pole. The contrast of neutral versus sad transgressor was associated with
significantly greater signal change in the SMG, bilateral insula, superior posteromedial
cortex (PMC) and the posterior cingulate cortex (CC). We did not find any significant
activations that survived threshold for the contrasts of sad versus happy, sad versus
neutral and neutral versus happy. (See also Figure 2-1 and Table 2-1).
43
Figure 2-1. Relative activations for judging happy versus sad transgressors (A), happy
versus neutral transgressors (B), and (C) neutral versus sad transgressors. Clusters
determined by z>2.3 and a (corrected) cluster threshold of p=0.05. Image is corrected for multiple
comparison using FDR at p=0.05. Note midCC: middle cingulate cortex; posCC: posterior
cingulate cortex; vmPFC: ventromedial Prefrontal cortex; SSII: secondary somatosensory cortex;
SMG: supramarginal gyrus; supPMC: superior posteromedial cortex.
Table 2-1. Coordinates of peak voxels from contrasts of happy versus sad transgressors,
happy versus neutral transgressors and neutral versus sad transgressors. Coordinates are
44
given in Montreal Neurological Institute (MNI) space. Z statistic images were thresholded using
clusters determined by z>2.3 and a corrected cluster significance threshold of p=0.05. Note
vmPFC: ventromedial prefrontal cortex; SFG: superior frontal gyrus; midCC: middle cingulate
cortex; SMG: supramarginal gyrus.
BOLD results from covariate analysis
Due to the behavioral findings mentioned in the previous section and our interest
in the neural regions engaged by participants whose judgment was influenced by the
transgressor’s emotional display, we performed a covariate analysis using participants’
rating of the happy and sad transgressors as an additional regressor in the group-level
GLM model. This allowed us to investigate signal changes in brain regions that would
have been more associated with the severity of moral judgment. We found that
participants who rated the sad transgressors less severely also recruited more the regions
of the right anterior insula (AI), orbitofrontal cortex (OFC) and the temporal pole. In
addition, participants who were more severe in their rating of the happy transgressors
recruited more the bilateral putamen, right amygdala and the left OFC when judging the
happy transgressors (see Figure 2-2).
45
Figure 2-2. Results of the covariate analysis revealing greater activation in the (A) bilateral
putamen, left orbitofrontal cortex (OFC) and right amygdala in participant who were more severe
in judging the happy transgressors; and (B) right anterior insula (AI), temporal pole, and right
OFC in participants who were less severe in judging the sad transgressors.
vmPFC signal change and ERA
ROI analysis revealed a significantly greater signal change in the vmPFC while
participants judged the happy transgressor compared to the sad transgressor (t(18)=4.022,
p=0.001) and the neutral transgressor (t(18)= -2.221,p=0.039). There was no significant
difference in signal change between judging the neutral and the sad transgressor
(t(18)=0.468,p=0.645; see figure 2-3). This differentiation in the vmPFC’s signal
strength and also in the temporal course of activation to the emotion conditions is
apparent in the ERA (Figure 2-4).
46
Figure 2-3. ROI analysis. The ventromedial prefrontal cortex (vmPFC) showed significantly
greater signal change while judging happy and sad transgressors as compared to neutral. Inset
depcits the vmPFC ROI which was functionally defined by the contrast of moral>banal and
anatomically delimited to each participant’s activation.
47
Figure 2-4. Event related average of signal extracted from the ventromedial prefrontal
cortex (vmPFC) ROI corresponding to the three conditions of neutral, happy and sad
transgressors. Panel at the bottom depicts stimulus events corresponding to the vmPFC time
course of activation. Time courses are normalized to the 10s preceding stimulus onset. On the x
axis, 0s represents onset of the video, first vertical black line marks the onset of transgression on
an average across all videos, second vertical black line marks the time of judgment and button
press by the participants.
vmPFC and behavioral variables
Participants who showed greater signal change in the vmPFC during judgment of
the sad transgressors also showed a trend towards perceiving the happy transgressor as
displaying less guilt (r=-0.432, p=0.065) and these participants also showed a trend
towards scoring highly on the Personal Distress component of the Interpersonal
Reactivity Index (IRI, Davis et al., 1980; r=0.343, p=0.151). The Personal Distress
subscale measures the “self-oriented” feelings of personal anxiety and unease in tense
48
interpersonal settings (i.e. ‘I sometimes feel helpless when I am in the middle of a very
emotional situation.”). In addition, participants who showed greater signal change in the
vmPFC while judging the happy transgressor also showed a trend of scoring highly on
the Fairness scale of the Moral Foundations Questionnaire (MFQ, Graham et al., 2009;
r=0.405, p=0.085). We did not find any significant correlation between the signal change
in the vmPFC in the three conditions and the severity of moral judgment (happy:
r=0.264,p=0.275; sad: r=0.071,p=0.772; neutral: r=0.103,p=0.675).
Discussion
Making moral judgment of a transgressor’s action involves a (1) cognitive
appraisal of another person’s situation and engaging in perspective taking, which may
preferentially recruit a network involving the rTPJ, STS, temporal pole, (2) experiencing
another’s emotion state, which may recruit the AI, the midCC, the PMC which are
affiliated with interoceptive information, and (3) using this emotion state as a platform to
decide the wrongness of the action, perhaps recruiting the OFC and the vmPFC (Moll et
al., 2005, Greene and Haidt, 2002). However, are these same brain regions engaged when
we make moral judgments of real-life situations? And can the transgressor’s emotion
display influence the participants’ emotion state and consequently her moral judgment of
the transgressor and the activity of these brain regions?
Transgressor’s emotion display influences participants’ moral judgment
Our findings may be elucidated to suggest, first that emotion displayed by the
transgressor influences the participants’ moral judgment of the transgressor’s action.
Participants were significantly more severe and took less time in their judgment of the
49
happy transgressor who benefitted from the transgression as compared to the sad or
neutral transgressors. Further, the happy transgressor was judged as showing the least
amount of guilt. These findings confirm our hypotheses and are in accord with
psychological accounts about the influence of guilt attribution and of action aversion on
moral judgment. Aligned with the behavioral findings, we also found that compared to
sad and neutral transgressors, judging the happy transgressors was associated with greater
signal change in brain regions involved in emotion processing (midCC, SSI, SSII),
decision making (vmPFC), and perspective taking (temporal pole, SMG). Interestingly,
compared to judging sad transgressors, judging neutral transgressors who displayed no
emotion was associated with greater signal change in the regions involved in
interoceptive processing (bilateral insula, midCC), musculoskeletal processing (superior
PMC) and “tuning” the focus of attention (posterior CC; Immorindo-Yang et al., 2009;
Leech and Sharp, 2014), suggesting that in the absence of emotion display, responding to
the neutral transgressor was made more cognitively demanding.
When participants were influenced by the transgressor’s emotion display in the
hypothesized direction, such that they were more severe in judging the happy
transgressor and less severe in judging the sad transgressor, they were more likely to
recruit brain regions involved in emotion processing, perspective taking and empathy
(right AI, bilateral putamen, right amygdala, temporal pole, OFC). These findings support
that we evaluate the moral status of another’s action by engaging in perspective taking
and simulating how we would feel when performing the action ourselves. In addition, we
note that the OFC was engaged by both, participants who judged the happy transgressor
more severely and those who judged the sad transgressor less severely.
50
We further inspected the influence of the transgressor’s emotion display on the
signal change in the vmPFC, a ROI, which we found to be engaged during moral
judgment of real-life situations. We found that participants took least time to respond to
the happy transgressors, and that this response to the happy transgressors was also
associated with greatest signal change in the vmPFC. Further, we found that the vmPFC
response to the happy transgressor peaked more quickly and for a shorter duration as
compared to the response to the sad transgressor which was more drawn out in time. The
participants who judged the happy transgressors more severely also recruited more the
right amygdala and the left OFC.
Implications for psychological and neural processes involved in moral judgment
Our findings indicate that observing happy transgressors that display no guilt
elicited little empathy in the participants who were able to make a quick and severe
judgment of the transgression. This is keeping with the evidence that the attribution of
guilt is positively related with sympathy and negatively related with anger (Carlo et al.,
2012), that intentional harm is perceived more severely than unintentional harm (Gray
and Wegner, 2008) and that the latter is related to empathic response elicited by the
immoral act. Our result is especially intriguing in light of the recent finding in moral
psychology that our aversion to a harmful immoral action has a critical influence on
moral judgment (Miller et al., 2014). The action aversion idea posits that the focus on the
intrinsically aversive nature of an immoral action is facilitated by a form of perspective
taking in which the participant adopts the agent’s point of view (Hannikainen et al.,
2013), and the negative emotional reaction arises from how bad it would feel to perform
51
that action, and hence, some actions just feel wrong (Cushman, 2012; Haidt et al., 2000;
Lieberman and Lobel, 2012). Consistent with the action aversion idea, our data suggest
that judging an action as wrong was not only influenced by how upsetting participants
considered the transgression to be but also, by the emotion display of the transgressor. A
positive emotion may have enhanced those effects. Not only did the happy transgressor
perform an aversive action but the display of positive emotion exacerbated the
aversiveness of the action, leading the participant to make a more severe judgment of the
happy transgressor than of the sad or neutral transgressors.
Support for the above interpretation was found not only in neural activation
patterns that covaried with participants severity of judgment, but also in vmPFC’s
activation pattern during participants’ judgment of the happy, sad and neutral
transgressors. Our findings are relevant to the literature concerning the role of the vmPFC
in moral judgment and social behavior. It has been shown that vmPFC damage may result
in high levels of aggressiveness, lack of concern for social norms (Bechara and Damasio,
2005; Damasio, 1999,1994) and there is general agreement that the vmPFC is crucial for
moral judgment (Ciaramelli and Pellegrino, 2011). Studies have demonstrated that
patients with vmPFC lesion are more likely to engage in a rational strategy in moral
decisions such as by endorsing killing one person to save multiple others (Koenigs et al.,
2007). At the same time, these patients seem to act less rationally than control subjects in
an economic game by more often turning down a sum of money to prevent receiving less
than another person (Koenigs and Tranel, 2007). Our findings are consistent with these
results in suggesting that the vmPFC plays a role in integrating affective and cognitive
52
information into subjective value signals that guide behavior (Hare et al., 2010;Damasio,
2000, 1996).
There is evidence that the amygdala responds preferentially to threatening stimuli
and that the connectivity between the amygdala and the OFC is greater during threat and
when a person’s painful situation is intentionally caused than when it is caused by
accident (Akitsuki and Decety, 2009). According to a more recent suggestion, during
moral judgment, the amygdala enables an automatic emotional response to a personally
harmful action whereas the vmPFC plays a more integrative role of weighing these
responses against each other (Shenav and Greene, 2014). These proposed mechanisms
are consistent with our finding of greater recruitment of the right amygdala and the left
OFC by the participants who judged the happy transgressors more severely. Moreover,
given the role of the OFC in effecting punishment through sentiments linked to aversion
such as anger, disgust and contempt (Zahn et al., 2009; Takahashi et al., 2008; Bechara et
al., 2000), our results suggests that perhaps, the display of emotion by the transgressor
exaggerated the affective response experienced during moral judgment.
One possible explanation is that evolutionarily, we learn to be aversive towards
free-riders or cheaters, and the display of happy emotion and no guilt by the transgressor
may co-opt neural mechanisms for responding to aversive situations more efficiently and
directly. On the other hand, responding to the sad transgressor while building on the same
neural mechanisms of moral judgment may operate less efficiently and directly as the
display of a sad emotion and perceived guilt may warrant additional time for further
processing of the transgressor’s action.
53
Additionally, claims regarding the role of the vmPFC in “mental time travel” or
anticipating future consequences (Ciaramelli and Pellegrino, 2011) may help explain the
different temporal course of vmPFC activation across the emotion conditions: while the
inherently aversive evaluation of the happy transgressor makes it easier to preclude the
future possibility of social exchange with the protagonist, the sad transgressor who is
perceived as displaying guilt is relatively harder to judge and predict. This interpretation
is supported by our finding that participants who judged the sad transgressor less severely
also engaged more the right AI, a region presumed to re-represent bodily states of arousal
and involved in empathy. These participants scored high on the personal distress
component of the IRI and rated the sad transgressors as displaying more guilt.
Taken together, the evidence from behavioral response and neural activity patterns
suggests that transgressors’ emotion displays influence participants’ judgment of their
transgressions, building from those related to immediate, severe judgments to those
which engage a greater and more drawn out processing of the psychological dimensions
of the scenario. This distinction was seen in the greater severity of the participants’
judgment and recruitment of brain regions related to emotion processing and perspective
taking during judgment of happy transgressors. Further, judging happy transgressors was
associated with greater and faster change in vmPFC activity as compared to judging sad
transgressors or transgressors with no emotion display.
54
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Chapter 3: Neural network connectivity and inter-network interactions during
moral judgments of real-life situations.
Abstract
Comprehending neural circuitry relies on knowledge of how dynamic interactions
between brain regions are associated with complex cognitive and emotional processes.
Here, we used independent component analysis to identify two distinct functional
networks, the default mode and the salience networks (DMN and SN respectively) during
rest. During fMRI scanning, participants made judgments of actors seen committing
moral transgressions in everyday life scenarios who displayed a positive or negative
emotion depending on whether or not their immoral acts were beneficial to them. Dual
regression analysis showed that in participants whose judgments were influenced by the
transgressor’s display of emotion, the DMN showed greater connectivity with brain
regions involved in emotion processing and perspective-taking in a separate resting-state
scan. Moreover, participants with stronger intrinsic connectivity between the DMN and
SN at rest were faster in responding to transgressors who displayed an emotion (happy or
sad), but not to the transgressor without an emotion display. Our findings suggest that
analysis of intra-network and inter-network connectivity may help elucidate the neural
architectures that support social and emotional functioning.
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Introduction
Complex operations such as perspective-taking, empathy and moral reasoning are
hallmarks of human cognition. These extraordinary and often unique human abilities are
made possible by interaction among several brain regions that belong to functionally
distinct neural networks. A better understanding of cognitive and emotional processes
such as moral judgment requires a knowledge of how brain regions and networks
functionally interact. There is an increasing interest in the analysis of brain networks to
characterize dynamic neural processes that engender complex human cognitive
capacities. Recently, this interest has percolated into research on moral judgment,
providing new insights into neural networks engaged during moral judgment and in
particular, studies have demonstrated the role of the default mode network (DMN) in
moral judgment (Reniers et al., 2012; Laird et al., 2011; Shilbach et al., 2008). The DMN
consists of a core of brain regions that are relatively more active during rest and the
performance of certain social and emotional tasks such as introspection, autobiographical
recall, perspective taking and moral judgment (Spreng et al., 2009;Harrison et al., 2008;
Buckner et al., 2008). Core areas of the DMN include the postero-medial cortices
(specifically the posterior cingulate cortex (PCC) and parts of the precuneus), the medial
prefrontal cortex (MPFC) as well as the inferior parietal lobule (IPL) extending into the
posterior temporal areas around the temporal-parietal junction (TPJ). Often the
hippocampus and adjacent medial temporal lobe and lateral temporal cortex expanding
into the temporal pole are also included in the DMN. These regions of the DMN have
been increasingly shown to be activated during tasks that require participants to
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understand and interact with others, perceive others’ emotion states, be empathic towards
others, infer others’ beliefs and intentions (Schilbach et al., 2008; Laird et al., 2011) and
more relevantly, during judgment of moral dilemmas. Decety and colleagues (2012)
found that functional connectivity between the vmPFC and TPJ was the strongest when
subjects viewed actions with moral valence as compared to actions without any moral
connotation. Nodes of the DMN were activated during hypothetical reasoning about
hypothetical moral dilemmas defined as being ‘personal,’ e.g. dilemmas in which the best
overall outcome can only be obtained through the violation of someone’s personal rights
(Greene et al., 2001, 2004). Research with adult and developmental psychopaths has also
provided support for the association between emotional impairment and defects in moral
judgment. Psychopathy is characterized by pronounced emotional impairment including
reduced empathy and guilt. In psychopaths, significantly reduced functional connectivity
has been reported between the medial frontal cortex (anterior DMN) and posterior brain
areas (posterior DMN) in the resting state (Pujol et al., 2012). Given the complexity of
moral reasoning, researchers have also investigated interaction of the DMN with regions
outside it and with other networks. Decety and colleagues (2012) found a positive age-
related increase in functional connectivity between the vmPFC and amygdala in response
to intentional harm.
The salience network which consists of the dorsal anterior cingulate cortex
(dACC) and the anterior insula (AI) was first described by Seeley and colleagues in 2007.
The SN is activated in response to various forms of ‘salience’ i.e. given the barrage of
internal and external stimuli that the organism is faced with, the SN works to identify and
orient to the homeostatically most relevant stimulus (Seeley et al., 2007). This stimulus
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could be an emotional dimension of pain (Peyron et al., 2000), empathy for pain (Singer
et al., 2004), metabolic stress, hunger, pleasurable touch (Craig, 2002), faces of loved
ones (Bartels and Zeki, 2004) or allies (Singer et al., 2004) and social rejection
(Eisenberger et al., 2003). Chiong and colleagues (2013) showed that using granger
causality analysis, that the salience network (SN) played a causal role in recruiting the
DMN during moral judgment in healthy subjects. However, this action was diminished in
patients with a behavioral variant of fronto-temporal dementia. These subjects showed
lower recruitment of the DMN, which the authors suggest, led to poor performance on
moral judgment tasks.
These preliminary studies highlight the need to investigate neural networks that
are engaged in healthy persons during moral judgment of the kind encountered in
everyday life. In the current study we pursued the following questions: (1) is there an
association between DMN connectivity patterns and moral judgment of real-life
situations? (2) is there a relationship between the interaction of the DMN and SN, and
moral judgment?
Critical to our aim is the use of everyday scenarios as stimuli as opposed to
stimuli usually used in fMRI studies of moral judgment (Greene et al., 2001). While in
the functional magnetic resonance imaging (fMRI) scanner, participants viewed video
clips depicting moral transgressions that may occur in everyday life, e.g. a student
hacking into his coach’s email. The transgressor displayed a happy facial expression
when s/he benefitted from the immoral act (e.g. he hacks into his coach’s email and finds
out he was selected to play in the final team), or a sad facial expression if s/he did not
benefit from the act (e.g. he was not selected to play in the final team), or no emotion at
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all. Specifically, we wanted to discern if the influence of the transgressor’s emotional
display on the observer’s moral judgment which we demonstrated in a separate study
(Singh et al., in prep), would be associated with patterns of DMN connectivity. We
hypothesized that subjects who were influenced by the transgressors’ display of an
emotion (those that judged more severely the happy transgressor and less severely the sad
transgressor), would show stronger coupling between the DMN and brain regions
engaged in emotion processing and perspective taking. In addition, we wanted to explore
the association between inter-network connectivity between the DMN and SN, and moral
judgment.
Method
Participants
Twenty right handed adults between the age of 18 and 35 years (average age: 24
years; 10 females) were recruited from the USC campus to participate in the study. To
ensure consistency along a cultural dimension, we included only those participants who
were born and raised in the USA with English as their first language. Participants were
excluded if they did not pass the MRI safety-screening questionnaire or if they had a
history of neurological or psychiatric disorder. All participants had normal or corrected
vision.
Stimulus preparation and selection
In collaboration with a professional film maker (Jeremy Kagan, Emmy-winning
director and head of the Change Making Media Lab of USC’s School of Cinematic Arts),
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we produced a variety of video clips that depict everyday situations with (i) neutral
content (e.g. student reading the letter in his mailbox); (ii) moral transgression without
any emotional content (e.g. student reading the letter in his professor’ mailbox); (iii)
moral transgression with emotional content (both positive and negative valence e.g.
student reading the letter in his professor’s mailbox and getting happy after finding out
that his professor wrote him a good recommendation, or getting sad after finding out that
his professor wrote him a poor recommendation). These video clips were edited to
include a short sentence explaining the situation along with an arrow that pointed at the
transgressor in the video so as to clearly identify him/her to the participant. The length
and duration of the text was controlled for each video clip to comprise on average equally
complex words and to be shown for 7sec. The arrow used throughout was the same (in
size, shape, onset of and 4 sec duration of appearance). The video clips were piloted to
ensure emotional effectiveness, moral content, arousal, and equivalence of visual
properties such as size, frame rate, brightness and contrast.
Experimental design and protocol
While inside the scanner, participants were presented with video clips in two
separate fMRI runs. Each video clip lasted 23 s, followed by a 13 s fixation cross on a
black screen, so that the entire duration of each trial was total 36 s. We prevented
repetition of videos belonging to the same category by using a pseudorandom order of
presentation. For each stimulus, participants were instructed to look at the video clip for
its entire duration. In the first functional run, participants saw 12 video clips, 4 each
belonging to the categories of neutral, happy, and sad emotion displayed by the
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transgressor. Following the presentation they answered the question ‘Did s/he (the
protagonist) do something wrong?’ Participants responded by button press to rate the
wrongness of the protagonist’s actions on a scale of 1 to 4, from “not wrong to “very
wrong”.
A second functional run was a 6 min long resting state scan. The participants
were instructed to “lay still with eyes open,” as they were presented with a blank white
screen with a black fixation cross in the center.
fMRI acquisition
A Siemens 3-Tesla MAGNETON TIM Trio scanner with a 12-channel matrix coil
at the Dana and David Dornsife Neuroimaging Centre at the University of Southern
California was used to collect imaging data. Functional scans were acquired using a T2*
weighted Echo Planar (EPI) sequence (TR = 2000ms, TE =30ms, Flip Angle = 90º) with
voxel resolution of 3mm by 3mm by 4.5mm. Thirty-two transverse slices were acquired
to cover the whole brain and brainstem. Anatomical scans were acquired using an
MPRAGE sequence (TI=900ms, TR=2530ms, TE =7ms, Flip Angle = 7º) with an
isotropic voxel resolution of 1mm.
Behavioral data analysis
We calculated across participants, for each condition (neutral, happy and sad
transgressor) the average severity ratings, reaction time, and guilt ratings.
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Group ICA
To identify functional brain network maps, we conducted a group Independent
Component Analysis (ICA) using the MELODIC toolbox in FSL (Version 3.12, FSL,
FMRIB's Software Library, www.fmrib.ox.ac.uk/fsl). ICA is a statistical technique that
allows exploration of whole brain networks without any a priori assumptions. ICA works
by decomposing a multivariate signal into temporally and spatially distinct components.
The following data pre-processing was applied to each subject’s input data: masking of
non-brain voxels; voxel-wise de-meaning of the data; normalization of the voxel-wise
variance. Pre-processed data were whitened and projected into a 20-dimensional
subspace using Principal Component Analysis.
The whitened observations were decomposed into sets of vectors which describe
signal variation across the temporal domain (time-courses), the session/subject domain
and across the spatial domain (maps) by optimising for non-Gaussian spatial source
distributions using a fixed-point iteration technique (Hyvärinen 1999). Estimated
component maps were divided by the standard deviation of the residual noise and
thresholded by fitting a mixture model to the histogram of intensity values (Beckmann
2004). Following the ICA, the component maps corresponding to canonical networks
were identified from artifactual components.
In order to explore the network interactions, we used dual regression, to derive
subject specific timecourses and spatial maps corresponding to each component
(Beckmann et al., 2009). Dual regression has been shown to be a reliable method that
produces more robust results than template matching ICA performed on an individual
level (Zuo et al., 2010). Dual regression works in two stages: (1) it regresses the group-
68
spatial-maps into each subject's 4D dataset to give a set of timecourses, and then (2) it
regresses those timecourses into the same 4D dataset to get a subject-specific set of
spatial maps (Beckmann, 2009; Filippini, 2009).
To test our hypothesis regarding correlations between DMN functional
connectivity and moral judgment, non-parametric tests were conducted voxel-wise via
the Randomize tool in FSL and using participants’ ratings of the transgressors as
covariates in the design matrix entered in dual regression. A total of 5,000 permutations
were carried out and significant clusters were corrected for multiple comparisons using
threshold-free cluster enhancement (Smith and Nichols, 2009)
To determine if there were relationships between moral judgment and inter-
network connectivity of the default mode and salience networks during rest, we
performed for each subject a partial correlation between the DMN and SN components’
timeseries of signal fluctuation (obtained via stage 1 of dual regression). Once we
acquired these partial correlation coefficients for each subject, we used them as
independent variables and correlated them with our behavioral variables. Thus, the
analysis indicated how the functional coupling between large-scale brain networks may
predict behavioral variables of interest.
Results
Behavioral results
As we had shown before, the happy was judged most severely and as showing the
least amount of guilt (F(2,38)=4.29,p=0.021; F(2,38)=4.67,p=0.015). We also found that
69
participants were fastest in their response to judge the happy transgressor
(F(2,38)=8.97,p=0.001).
ICA Results
The group ICA produced a total of 20 independent components from which we
visually identified the ones corresponding to the networks of interest namely the default
mode network (component number 4, fig3-1A) and the salience network (component
number 5, fig3-1B). The remaining components were identified as corresponding to other
functional networks or deemed to represent artifactual (e.g. physiological) noise due to
predominant activation in the white matter, ventricles, or vasculature, head movement or
signal dropout.
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Spatial differences in network functional connectivity related to behavioral measures.
We hypothesized that participants whose moral judgment was influenced by
emotion display of the transgressor would show stronger coupling at rest between the
DMN and the regions of the brain engaged in emotion processing and perspective taking.
Probing the connectivity of DMN as outputted in stage 3 of the dual regression, we
sought neural regions whose functional connectivity with the DMN in the undirected
resting state was correlated with (a) more severe rating of the happy transgressor and (b)
less severe rating of the sad transgressor. As shown in Figure 2a, more severe rating of
the happy transgressor correlated with stronger coupling at rest between the DMN and
the right anterior insula, anterior cingulate cortex (ACC), posteromedial cortex (PMC),
right hippocampus and the ventromedial prefrontal cortex (vmPFC). In addition, less
severe rating of the sad transgressor correlated with stronger functional connectivity at
rest between the DMN and the supramarginal gyrus (SMG), right anterior insula, left
amygdala and the inferior PMC (see figure 3-3).
71
Correlating inter-network coupling with behavioral measures.
To examine if the moral judgment measures were associated with the inter-
network functional coupling between the DMN and SN, we conducted a simple
correlation analysis. Once we acquired the partial correlation between the DMN and SN,
we treated it as our independent variable and correlated it with behavioral variables. We
found that stronger DMN-SN coupling at rest correlated significantly with faster response
time to judging happy and sad transgressors (r=-0.454, p=0.051; r=-0.490,p=0.033) but
not neutral transgressors (r=-0.273,p=0.278). We did not find any significant correlation
between DMN-SN coupling and severity of moral judgment (happy: r=0.375,p=0.114;
sad: r=0.068,p=0.784; neutral: r=0.04, p=0.848).
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Discussion
Our study establishes a link between functional connectivity of neural networks,
and moral judgment of real life situations. We mapped independent components during
undirected mental activity and identified distinct functional networks that are critical in
the guidance of thought and behavior (Seeley et al., 2007; Ridderinkhof et al., 2004b;
Critchley, 2005). These networks reflect midline internal-state oriented processing and
the paralimbic emotional salience processing, the default mode network (DMN) and the
salience network (SN) respectively. In contrast to cognitive subtraction paradigms
employed by most neuroimaging studies, our approach of ICA with dual regression
allowed us to look at interaction between distinct neural networks.
First, we found that individuals whose moral judgment was influenced by the
transgressor’s emotion display, showed stronger coupling of the DMN with regions
including the vmPFC which has been shown to be involved in the integration of
representations of other’s intentions with their outcomes during social-decision making
(Cooper et al., 2010), and the ACC which has been implicated in theory of mind, self-
referential processing (Frith, 2001). In addition, our results show regions of the anterior
insula, which is involved in visceral somatosensation, emotional feeling and regulation
and empathy (Immordino-Yang and Singh, 2011), the hippocampus which is engaged
during processing of complex social emotions such as compassion and admiration
(Immordino-Yang and Singh, 2013) and the amygdala, a subcortical structure that is
engaged in evaluation of moral judgment (Greene et al., 2004) and also in empathic
sadness during morally-salient scenarios (Decety et al., 2011). It has previously been
reported that in healthy subjects, the DMN is recruited during deliberation on personal
73
moral dilemmas (Reniers et al., 2012; Laird et al., 2011; Shilbach et al., 2008). Brain
regions including the vmPFC, ACC, anterior insula, the hippocampus, and regions
comprising the DMN have been shown to be involved in cognitive operations of inferring
other people’s states of mind- engaging in dynamic simulation in order to fully
experience the other person’s perspective (Spreng and Grady, 2010; Buckner et al.,
2008). Our results suggest that individuals whose moral judgment was influenced by the
transgressor’s emotional display were also more likely to engage in greater mental
simulation and inferring of the transgressor’s point of view.
Second, we found that individuals who showed stronger coupling at rest between
the DMN and the SN also took less time in responding to those transgressors who
displayed an emotion, (happy or sad), but this relationship did not hold for the neutral
transgressor. The SN has been implicated in alertness, attention and switching between
the default mode network and the executive control network (Dosenbach et al., 2006;
Seeley et al., 2007; Sridharan et al., 2008; Menon & Uddin, 2010). We suggest that the
SN plays an alerting role such that it identifies the emotion displayed by the transgressor
and then recruits the DMN (Chiong et al., 2013). This explanation is supported by the
finding that SN dysfunction in patients with behavior variant fronto-temporal dementia
results in a failure to recognize the emotional nature of personal moral dilemmas,
consequently leading to a failure in recruiting the DMN and deficits in moral judgment.
In summary, using ICA and dual regression, we identified two functionally
distinct neural networks during rest, namely the DMN and the SN. These networks were
correlated with the subjects’ moral judgment of real-life situations. Further investigation
of neural networks will help understand their role in human emotion and cognition and
74
how differences in these networks’ connectivity may relate to individual differences in
thought, feeing and action patterns.
75
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and dual regression approach. Neuroimage, 49(3), 2163-2177.
80
Chapter 4: Role of empathic patterns and emotion in moral judgments of real-life
situations
Abstract
Empathy refers to our ability to understand and share the emotional states of other
persons (Batson, Fultz, and Schoenrade, 1987; Decety and Jackson, 2004). From a moral
point of view, it is widely believed that empathy is a good thing, and sometimes, it is
suggested as necessary for morality. Recently, studies in psychology have shown that
empathy may motivate moral conduct (DeSteno et al., 2012), however, little is known
about how individual differences in trait empathy may correlate with moral judgment.
Here we quantified participants’ trait level empathy and found that participants whose
moral judgments were influenced by the moral transgressor’s emotion display also had a
high score on measures of trait empathy. These findings suggest that observers’ trait
empathy may be associated with differential moral judgment of transgressors displaying
different emotions.
Introduction
Empathy is critical for creating and maintaining human bonds, by enabling people
to comprehend, share and respond to another person’s emotional state. Historically,
empathy has been viewed as a necessary condition for moral behavior (Aristotle/Roger,
2000; Hume, 1960/1777; Smith, 1853). However, more recent empirical work provides
mixed evidence regarding the role of empathy in moral judgment (Hauser, 2006; Prinz,
2004).
81
We know that empathy allows an affective sharing or resonating of an observer
with another person’s emotional state, thereby allowing the observer to understand what
it feels like to be in the other person’s situation. Often a distinction is made between
cognitive and affective forms of empathy. While cognitive empathy may be understood
as simply knowing how the other person feels and what s/he may be thinking, affective
empathy involves understanding the other person’s emotion state and feeling along with
her/him (Goleman, 2007). By allowing the observer to understand how others are
emotionally affected by a given action, empathy can inform moral judgments. Therefore,
we hypothesized that individuals high in trait empathy will be more likely to be
influenced by the transgressor’s emotional display and this will affect their moral
judgment. We decided to obtain a nuanced measurement of individual differences in trait
empathy and correlate this to their moral judgment of real-life moral transgressions. We
quantified individuals’ trait level empathy using measures of the interpersonal reactivity
index (IRI, Davis, 1980), resting cardiac vagal tone and individuals’ neural response to
videos designed to elicit compassion for physical pain. Resting cardiac vagal tone is a
biological predisposition that modulates emotion behavior. It is an index for the constant
parasympathetic input to the heart via the vagus nerve. The resting cardiac vagal tone
maintains a relatively slow resting heart rate by inhibiting spontaneous firing rate of the
cardiac pacemaker at the sinoatrial node, thereby serving as a protective vagal “break.”
By increasing or decreasing rapidly to modulate heart rate, the vagal break allows an
individual to engage or disengage with a stimulus depending on situational needs
(Porges, 2001). Therefore, resting cardiac vagal tone reflects the flexibility of
parasympathetic modulation. It has been shown to be a positive factor for emotion
82
regulation and prosocial behavior (Porges, 2007, Eisenberg et al., 1996). Higher resting
cardiac vagal tone has been linked to less negative emotional expression during social
interactions (Pu, Schmeichel, and Demaree, 2010). In addition, as a proxy for physical
empathy, we recorded participants’ neural activation patterns in response to videos
designed to elicit compassion for physical pain (CPP, Immordino-Yang et al., 2009).
These CPP videos depict people sustaining injuries and emotions about others’ physically
painful predicaments (elicited by CPP videos) co-opt neural mechanisms for personally
experienced pain (Immordino-Yang et al., 2009). Here, we were specifically interested in
the region of the ventral anterior insula, which is known be involved in emotion feeling
and empathy (Kurth et al., 2010).
We hypothesized that observers high in trait level empathy will be influenced by
the transgressor’s emotion display while making their moral judgment, such that they will
be more severe in their judgment of the happy transgressor and less severe in their
judgment of the sad transgressor.
Method
Participants
Twenty right-handed adults between the age of 18 and 35 years (average age: 24
years; 10 females) were recruited from the USC campus to participate in the study. To
ensure consistency along a cultural dimension, we included only those participants who
were born and raised in the USA with English as their first language. Participants were
excluded if they did not pass the MRI safety-screening questionnaire or if they had a
83
history of neurological or psychiatric disorder. All participants had normal or corrected
vision.
Stimulus preparation and selection
In collaboration with a professional film maker (Jeremy Kagan, Emmy-winning
director and head of the Change Making Media Lab of USC’s School of Cinematic Arts),
we produced a variety of video clips that depict everyday situations with (i) neutral
content (e.g. student reading the letter in his mailbox); (ii) moral transgression without
any emotional content (e.g. student reading the letter in his professor’ mailbox); (iii)
moral transgression with emotional content (both positive and negative valence; e.g.
student reading the letter in his professor’s mailbox and getting happy after finding out
that his professor wrote him a good recommendation, or getting sad after finding out that
his professor wrote him a poor recommendation). These video clips were edited to
include a short sentence explaining the situation along with an arrow that points at the
transgressor in the video to identify him/her to the observer. The length and duration of
the text was controlled for each video clip to comprise on average equally complex words
and to be shown for 7sec. The format, shape, onset, duration, speed and color of the
arrow was also controlled throughout each clip by using the same sized and shaped blue
arrow, that appears at the start of each clip for a duration of 4 sec. The video clips were
piloted to ensure emotional effectiveness, moral content, arousal, and equivalence of
visual properties such as size, frame rate, brightness and contrast.
84
Experimental design and protocol
While inside the scanner, participants were presented with video clips in two
separate fMRI runs. Each video clip lasted 23 s, followed by a 13 s fixation cross on a
black screen, so that the entire duration of each trial was total 36 s. Participants were
shown the video clips in pseudorandom order (different for each participant) to avoid
repetition of videos belonging to the same category. For each stimulus, participants were
instructed to look at the video clip for its entire duration. In the first functional run,
participants watched video clips in which the emotion of the moral transgressor was
manipulated. In this run, participants saw 12 video clips, 4 each belonging to the
categories of neutral, happy, and sad transgressor emotions. Following the clip they
answered the question ‘Did s/he do something wrong?’ (referring to the protagonist) .
Participants responded by pressing buttons on the button box by rating the wrongness of
the protagonist’s actions on a scale of 1 to 4, from “not wrong to “very wrong”. In the
second functional run, participants were presented with CPP videos and control videos
that depicted similar actions but without any injury to the protagonist. Following the 5 s
long video clips was a black screen during which participants were instructed to indicate
how emotionally moved they were by the video on a scale of 1 to 4, from: “not moved at
all” to “very moved.”
After scanning, participants were debriefed and presented with the videos again
and asked to respond, on a scale of 1 (not at all) to 4 (very much), to the questions of
“Does s/he look happy?” “Does s/he look sad?” “Does s/he look guilty?” The first two
questions were to establish the validity of the emotion display by the transgressor while
the third question was to quantify the guilt the observers’ thought the transgressor
85
displayed. In addition, participants filled out behavioral questionnaires including the
Interpersonal Reactivity Index (IRI, Davis et al., 1980) and the Moral Foundations
Questionnaire (MFQ, Graham et al., 2009).
fMRI acquisition
A Siemens 3-Tesla MAGNETON TIM Trio scanner with a 12-channel matrix coil
located at the Dana and David Dornsife Neuroimaging Centre at the University of
Southern California was used to collect imaging data. Functional scans were acquired
using a T2* weighted Echo Planar (EPI) sequence (TR = 2000ms, TE =30ms, Flip Angle
= 90º) with voxel resolution of 3mm by 3mm by 4.5mm. Thirty-two transverse slices
were acquired to cover the whole brain and brainstem. Anatomical scans were acquired
using an MPRAGE sequence (TI=900ms, TR=2530ms, TE =7ms, Flip Angle = 7º) with
an isotropic voxel resolution of 1mm.
Behavioral data analysis
For the first functional run, which comprised videos depicting a transgression but
differing in the presence of emotional content, we calculated across participants, for each
condition (neutral, happy and sad transgressor) the average severity ratings, reaction time,
and ratings for guilt.
For the second functional run, we calculated the participants’ strength of emotion
experience in response to the CPP videos and the time taken to press the appropriate
button.
86
fMRI image processing
Data were preprocessed and analyzed using FEAT (FMRI Expert Analysis Tool)
Version 5.98, part of FSL (FMRIB’s Software Library, www.fmrib.ox.ac.uk/fsl). After
motion correction, images were temporally high-pass filtered with a cutoff period of 200s
and smoothed using a 8mm Gaussian FWHM algorithm in 3D. The BOLD response was
modeled using a separate explanatory variable (EV) for each stimulus type (moral
transgression and banal for functional run 1, neutral, happy and sad transgressor for
functional run 2). For each stimulus type, the presentation design was convolved with a
gamma function to produce an expected BOLD response. The temporal derivative of this
timecourse was also included in the model for each EV. Data were then fitted to the
model using FSL’s implementation of the general linear model.
Each participant’s statistical data were then warped into a standard space based on
the MNI-152 atlas. We used FLIRT (FMRIB’s Linear Image Registration Tool) to
register the functional data to the atlas space in three stages. First, functional images were
aligned with the high-resolution coplanar T2-weighted image using 6 degrees of freedom
rigid-body warping procedure. Next, the coplanar volume was registered to the T1-
weighted MP-RAGE using 6 degrees of freedom rigid-body warp. Finally, the MP-
RAGE was registered to the standard MNI atlas with 12 degrees of freedom affine
transformation.
Random effects higher-level analysis was carried out using FLAME (FMRIB’s
Local Analysis of Mixed Effects) (Behrens, Woolrich, and Smith, 2003). For our whole-
brain analysis, statistical images were thresholded using clusters determined by Z > 2.3
and a corrected cluster size significance threshold of P = 0.05 (Worsley, Evans, Marrett,
87
and Neelin, 1992; Friston, Worsley, Frackowiak, Mazziotta, and Evans, 1994; Forman et
al., 1995).
Region Of Interest (ROI) definition and analysis
To define the apriori region of interest, the bilateral ventralAI, we employed a
previously defined anatomical mask (Immordino-Yang, Yang, and Damasio, 2014),
binarized it and then constrained it by multiplying it with the average activation across
participants in the contrasts of CPP versus control (see Figure 4-1). To investigate how
the activity of the ventralAI correlated with behavioral measures, we used Featquery
(FSL) to extract, for each participant, parameter estimates corresponding to the CPP
versus baseline in run 2.
Figure 4-1. Views of the ventral anterior insula region of interest depicted on the MNI
brain template.
Baseline ECG recording
Following the interview, participants underwent a baseline ECG recording session
for calculation of resting cardiac vagal tone. During the recording, participants were
instructed to relax with their eyes closed and to synchronize their breaths to an audio cue
88
(delivered at 0.25 Hz) for 5 minutes. ECG was measured using three MRI-compatible
electrodes placed on the participant’s chest and sampled at a rate of 1000 Hz.
Calculating resting cardiac vagal tone
ECG recordings were preprocessed in Acqknowdge 9.32 (Biopac) to identify the
R peaks of the QRS complex. The resulting intervals between R peaks were plotted and
visually inspected for artifacts. Misidentified R peaks were manually corrected. Using
Kubios HRV (kubios.uku.fi/), RR interval series were then uniformly re-sampled at 4 Hz
using cubic spline interpolation, detrended using smooth prior regularization (Tarvainen,
Ranta-Aho, and Karjalainen, 2002) to remove slow fluctuation, and participanted to
power spectrum analysis using fast Fourier transformation. High frequency (0.15-0.4 Hz,
roughly the breathing frequency) power of RR interval time series was used as a measure
of resting RSA.
Resting RSA is expressed both in normalized units and in absolute high frequency
power (Task Force of the European Society of Cardiology and the North American
Society of Pacing Electrophysiology, 1996). RSA in normalized units (RSAnu) was
calculated as the relative value (in percentage) of high frequency power in proportion to
the total power minus the very low frequency (< 0.04 Hz) power. Representation of RSA
in normalized unit is thought to reflect the balanced behavior of the two main branches of
the autonomic system (Malliani, Pagani, Lombardi, and Cerutti, 1991; Pagani et al.,
1986). This calculation minimizes the effect of changes in total power on RSA measure
(Task Force of the European Society of Cardiology and the North American Society of
Pacing Electrophysiology, 1996).
89
Absolute high frequency power was log transformed to improve statistical distribution
(RSALN), as recommended by Lewis, Furman, McCool, and Porges (2012).
Results
Trait level empathy and moral judgment
We found no correlation between individual resting cardiac vagal tone and
severity of moral judgment (neutral: r=0.326; p=0.278, happy: r=0.086; p=0.779, sad: r=-
0.014; p=0.963). Individuals’ scores on the empathic concern and perspective taking
component of the IRI were correlated to the severity of moral judgment of the happy and
neutral transgressor (r=0.577,p=0.008; r=0.402,p=0.07) but not the sad transgressor
(r=0.372; p=0.107). Individuals with high scores on these IRI components also scored
highly the MFQ harm and fairness scales (r=0.509, p=0.02; r=0.664, p=0.001), and they
responded faster to the CPP videos (r=-0.442, p=0.051). There was also no significant
correlation between individuals’ severity of moral judgment and their rating of or
response time to the CPP videos (neutral: r=0.121,p=0.611, r=0.187,p=0.429; happy: r=-
0.068, p=0.774, r=0-.349, p=0.139; sad: r=0.116, p=0.626, r=-0.054; p=0.822). As
hypothesized, signal change in the ventral AI ROI in response to the CPP videos
correlated with stronger rating of the CPP videos (r=0.397, p=0.093) and also with rating
the sad transgressors as displaying more guilt (r=0.469, p=0.043).
90
Discussion
Our study provides demonstrates that observer’s moral judgment is associated
with their trait level empathy during moral judgment.
First, we found that participants who (1) scored high on the EC and PD components of
the IRI and (2) engaged more the ventral AI, the brain ROI involved in empathy in
responding to the CPP videos, were influenced by the emotion displayed by the
transgressor. Specifically, individuals high in trait empathy were more severe in the
judgment of the happy transgressor and less severe in their judgment of the sad
transgressor. We found that observers who showed greater strength of signal change in
the ventral AI in response to CPP videos also rated the sad transgressor as displaying
more guilt and showed greater signal change in the vmPFC while judging the happy
transgressor. The ventral AI has been suggested to indicate the strength of emotion
feeling and known to be involved in empathy (Immordino-Yang et al., 2014; Kurth et al.,
2010). These correlations between the behavioral and neural responses argue that the
observers who were high in empathic tendency were more likely to be influenced by the
emotion display of the transgressor, and as hypothesized, this influenced the observers’
moral judgment. Empathy affords the observers information about the internal affective
states of the transgressors, providing feedback about the outcome of the transgressor’s
action. According to Hume (1777/1960), empathy elicits feelings of approbation or
disapprobation and this can be used to decide whether an action should be considered
morally right or wrong. Following this, we reason that the vicarious feeling experienced
by the observers in response to the happy transgressor’s emotion display and action was a
negative one and motivated the observer to judge the action as being morally wrong and
91
the transgressor as displaying less guilt. On the other hand, the sad transgressors were
perceived as displaying more guilt and becoming aware of the transgressor’s sad emotion
motivated a less severe moral judgment by the observers.
The correlations revealed by our results raise an important question of whether
different empathic patterns may correlate with the differences in the influence of emotion
on moral judgment For example, are different patterns of empathy engaged when
observers judge the happy transgressor more severely than when they judge the sad
transgressor less severely?
In future studies, we would need to measure both cognitive and affective forms of
empathy and correlate them with the observers’ moral judgment. It is also possible that
the differential effect of cognitive and affective empathy on moral judgment may be
related to different sectors of the anterior insula. Recently, Immordino-Yang and
colleagues (2014) found that the ventral and dorsal sectors of the anterior insula (vAI and
dAI respectively) contribute differently to emotion feeling, and that the relative
contributions are influenced by social context of culture. In their study, activity in the vAI
correlated with feeling strength, and the timing of activity in the dAI correlated with the
time at which participants reported their emotional feeling. These associations however,
were influenced by and varied across the participants’ cultural groups. The findings of
Immordino-Yang et al. (2014) have important implications for our study. How may these
sectors of the AI relate to the difference in the influence of emotion on moral judgment?
For example, it may be that judging the happy transgressor, which takes less time and
elicits a more severe judgment, may rely on a cognitive form of empathy and engage the
92
dAI, whereas judging the sad transgressor, which takes more time and elicits a less severe
judgment, may rely on affective empathy and engage more the vAI.
Despite these open questions, the study suggests that individuals’ trait empathy is
associated with the influence of emotion on moral judgment of real-life situations.
93
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Chapter 5: Behavioral indicators of cognitive processing during moral judgment of
real-life situations.
Abstract
Gaze direction and speech rate serve several important functions in complex
social interaction such as regulation of interaction, facilitation of communication goals,
and expression of intimacy and social control (Kleinke, 1986). Recently, gaze direction
has been shown to be indicative of a concrete or abstract level of cognitive processing
(Spellman and Schnall, 2009; Immordino-Yang et al., in prep). For example, gazing out
of the immediate context has been linked to abstract, higher-level processing such as
when feeling admiration in reaction to someone’s virtuous acts (Immordino-Yang et al.,
in prep.). Making moral judgments of real-life situations too involves abstraction:
participants are likely to think about ‘why’ the transgressors perform the immoral act, to
engage in perspective taking and to understand how the transgressors’ actions makes
them feel. Accordingly, we expected that participants’ eye gaze and speech rate would be
associated with the influence of emotion on moral judgment of real-life situations. We
quantified participants’ eye gaze and speech rate during a video-taped interview wherein
they discussed their judgments of transgressors’ actions. We found that participants who
judged the happy but not the sad transgressors as displaying less guilt were more likely to
avert their gaze. In addition, gaze aversion was associated with slower speech rate across
all three conditions of judging the happy, sad and neutral transgressor.
97
Introduction
Speech rate and visual communication signals such as eye gaze and behavioral
expressions (e.g. facial expressions, body posture) are important sources of information,
and have been suggested to play a facilitatory role in human communication (e.g. Clark
and Brennan, 1991; Goldin-Meadow, Wein, and Chang, 1992). These signals are
informative in that they carry information about cognitive load, for e.g. cognitive
difficulty of a task relates to the likelihood that the person will avert his gaze from other
people’s faces (Doherty-Sneddon, Bruce, Bonner, Longbotham, and Doyle, 2002).
Excessive eye gaze between speakers is associated with increased cognitive load as
evidenced by less fluent speech (Beattie, 1981). In addition, gazing out of the immediate
context, away from the interlocutor, has been linked to abstract processing and efficient
retrieval of information from long-term memory (Doherty-Sneddon and Phelps, 2005;
Glenberg, Schroeder, and Robertson, 1998). While considerable research effort has been
expended on examining the role of eye gaze, speech rate in human interaction, these non-
verbal and verbal indicators remain unexamined in the context of emotion processing.
Exceptionally, work from our lab (Immordino-Yang, Pavarini and Schnall, in prep) has
shown that when discussing others’ virtuous acts, which triggered positive emotions,
participants were more likely to avert their gaze and gaze aversion was associated with
use of abstract, morally relevant language. To explain their results, the authors applied
the construal level theory (CLT, Trope and Liberman, 2010), according to which actions
can be represented as high-level construals that involve abstract, superordinate
representations focusing on the underlying purpose and ‘why’ someone performed an
action, by contrast, low-level construals refer to concrete, contextualized representations
98
that focus on ‘how’ the action was performed. High-level construals involve abstraction,
increase psychological distance by removing people from the ‘here and now,’ and people
tend to broaden their psychological horizons such as in space and time (Trope and
Liberman, 2010). Further, based on the embodied cognition framework, high-level
processing is revealed by non-verbal indicators such as gazing away and up to higher
space, expressions such as “moral high-ground” (e.g. Barsalou, 2008; Spellman and
Schnall, 2009). For example, Meier and colleagues (2007) found that words referring to
virtues are categorized more quickly if presented in a high (versus low) position on a
screen.
We know that moral judgment involves abstraction: making them requires us to
engage in perspective taking to understand another person’s situation, relate it to our own
emotion state and feeling, and use our experiences of living in the social world to then
arrive at a judgment.
Accordingly, we expected that when making moral judgments participants would
engage in abstract construal-level processing that would be behaviorally manifested in
change in eye gaze and speech rate.
Method
Participants
Twenty right-handed adults between the age of 18 and 35 years (average age: 24
years; 10 females) were recruited from the USC campus to participate in the study. To
ensure consistency along a cultural dimension, we included only those participants who
99
were born and raised in the USA with English as their first language. Participants were
excluded if they did not pass the MRI safety-screening questionnaire or if they had a
history of neurological or psychiatric disorder. All participants had normal or corrected
vision.
Stimulus preparation and selection
In collaboration with a professional film maker (Jeremy Kagan, Emmy-winning
director and head of the Change Making Media Lab of USC’s School of Cinematic Arts),
we produced a variety of video clips that depict everyday situations with (i) neutral
content (e.g. student reading the letter in his mailbox); (ii) moral transgression without
any emotional content (e.g. student reading the letter in his professor’ mailbox); (iii)
moral transgression with emotional content (both positive and negative valence; e.g.
student reading the letter in his professor’s mailbox and getting happy after finding out
that his professor wrote him a good recommendation, or getting sad after finding out that
his professor wrote him a poor recommendation). These video clips were edited to
include a short sentence explaining the situation along with an arrow that points at the
transgressor in the video to identify him/her to the observer. The length and duration of
the text was controlled for each video clip to comprise on average equally complex words
and to be shown for 7sec. The format, shape, onset, duration, speed and color of the
arrow was also controlled throughout each clip by using the same sized and shaped blue
arrow, that appears at the start of each clip for a duration of 4 sec. The video clips were
piloted to ensure emotional effectiveness, moral content, arousal, and equivalence of
visual properties such as size, frame rate, brightness and contrast.
100
Experimental design and protocol
While inside the scanner, participants were presented with 23 s long video clips
followed by a 13 s fixation cross on a black screen, so that the entire duration of each trial
was total 36 s. Participants were shown the video clips in pseudorandom order (different
for each participant) to avoid repetition of videos belonging to the same category. For
each stimulus, participants were instructed to look at the video clip for its entire duration.
The outcome of the transgression was manipulated such that the transgressor displayed a
happy (if the outcome was beneficial) or sad (if the outcome was not beneficial) or no
emotion. Participants saw 12 video clips, 4 each belonging to the categories of happy, sad
and neutral transgressor. Following the clip they answered the question ‘Did s/he do
something wrong?’ (referring to the protagonist) . Participants responded by pressing
buttons on the button box by rating the wrongness of the protagonist’s actions on a scale
of 1 to 4, from “not wrong to “very wrong”.
After scanning, participants were debriefed and during a video-taped interview
with the experimenter, presented with the videos again and asked to respond, on a scale
of 1 (not at all) to 4 (very much), to the questions of “Does s/he look happy?” “Does s/he
look sad?” “Does s/he look guilty?” The first two questions were to establish the validity
of the emotion display by the transgressor while the third question was to quantify the
guilt the observers’ thought the transgressor displayed.
101
Behavioral data analysis
We calculated across participants, for each condition (neutral, happy and sad
transgressor) the average severity ratings, reaction time, and ratings for guilt.
Interview Procedure
Following fMRI scanning, each individual participant was video-taped during an
interview about his/her emotional reactions to 12 videos (4 for each condition) depicting
moral transgressions. During the interview, the participant was seated to the right of the
experimenter at a table, allowing both the experimenter and participant to comfortably
watch the video materials on the laptop directly in front of the participant. To the right of
the participant was a nondescript office wall; above was a white ceiling. The
experimenter presented each video in the same order that the participant saw them in
while inside the scanner. After the video ended, the experimenter reminded the
participant of the rating given to each video while inside the scanner and asked the
participant to respond to “Why did you give her/him that rating?” and on a scale of 1 (not
at all) to 4 (very much), to the questions of “Does s/he look happy?” “Does s/he look
sad?” “Does s/he look guilty?” While the participant answered, the experimenter then
unobtrusively gazed downward into her notebook and took notes. This was done so to
avoid inadvertently influencing the participant’s behavior during the response phase.
The videotaped interviews were later transcribed, and transcriptions were
independently verified.
102
Measures
Eye gaze. We coded participants’ eye gaze while they described their reaction to
each moral transgression from the video with the sound turned off. For each video that
the participants discussed, they were given a score of 0 if their gaze remained either
toward the experimenter or to the computer screen. On the other hand, for gaze aversion
in which a participant shifted their gaze either upward (towards the blank ceiling) or to
the right (towards a nondescript wall) was scored 1. By default, participants would start
describing their feelings looking frontward and down to the computer screen depicting
the narrative materials, or toward the experimenter, who was sitting to the participant’s
left. Thus, participants received a score of 0 for each narrative response in which gaze
remained either toward the experimenter or to the computer screen. Gaze aversion was
defined as shifting their gaze either upward (toward the blank ceiling) or to the right
(toward a nondescript wall) while responding, in which case participants received a score
of 1. Each participant received an overall score for each video category which was
calculated by taking the sum of the top three scores in each video category.
Speech rate. Participants’ speech rate was computed by dividing the number of
words spoken by the duration of the answer (in seconds). The duration was measured
from the end of the experimenter’s utterance (‘How wrong do you think his/her actions
are?’ ‘How do they make you feel?’) to the end of participant’s last utterance.
Participants’ answers were uninterrupted except on occasions when they asked the
experimenter clarifying questions (e.g. Do I need to rate on a scale of 1-4?). This measure
implies that a low speech rate reflects both low pronunciation speed and high number of
pauses inter-words or inter-phrases. Participants’ answers were normally uninterrupted,
103
but on rare occasions they asked clarifying questions to the experimenter (e.g., ‘Did the
bag belong to her?’). In these cases, the time spent by the experimenter to answer the
question was subtracted from the answer duration. For each participant, mean speech rate
in words per second was computed for each condition of happy, sad and neutral
transgressor.
Results
Natural, spontaneous behavior and moral judgment
Individuals who displayed higher gaze aversion during their judgment, showed a
trend in rating the happy transgressor as displaying less guilt (r= -0.344, p= 0.137) and
taking less time (r= -0.328, p=0.159) to judge the happy transgressors. In addition,
individuals who displayed higher gaze aversion also had a slower speech rate while
making their moral judgment (neutral: r=-0.509,p=0.031; happy: r=-0.458, p=0.056; sad:
r=-0.446, p=0.063).
Discussion
We were interested in how the observers’ spontaneous behaviors including eye
gaze and speech rate may be associated with their moral judgment. We found that
depending on the emotion displayed by the moral transgressor, there is a difference in
whether observers engage in high-level abstract processing while judging. Observers who
rated the happy transgressors as displaying less guilt were more likely to avert their gaze
and gaze aversion in our observers was associated with a slower speech rate. This
indicates that compared to sad or neutral transgressors, judging and discussing their
104
judgment of the happy transgressor required the observers to disengage from the
immediate environment to interpret the transgressor’s actions in the light of abstract
moral norms, looking beyond how the action was performed, and instead attempting to
understand the mind states of the transgressor and why s/he performed the immoral
action. The construal level theory supports this interpretation in that gaze aversion or
disengaging from the physical environment is conducive to high level construal based
cognitive processing. However, it is not clear why similar results were not seen with
respect to judging the sad transgressor, which like judging the happy transgressor would
also be expected to rely on more abstract cognitive processing than judging the
transgressor with no emotion. We can also understand our findings in light of the
embodiment literature: gazing away and up may be considered a non-verbal indicator of
high-level processing.
In interpreting these results, we should note the need for greater investigation into
the meaning of verbal and non-verbal cues such as gaze aversion and speech rate in the
context of emotion processing. Notwithstanding, the relationships found in this study
raise further questions about whether gaze aversion can differentiate between
abstract/moral and concrete –oriented cognitive processing during other social contexts.
105
References
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qualitatively distinct vicarious emotions with different motivational
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Levine, and S.D. Teasley (Ed’s), Perspectives on Socially Shared Cognition. (pp.
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Decety, J., and Jackson, P. L. (2004). The functional architecture of human
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DeSteno, D., Breazeal, C., Frank, R. H., Pizarro, D., Baumann, J., Dickens, L, and Lee, J.
(2012). Detecting the trustworthiness of novel partners in economic exchange.
Psychological Science, 23, 1549-1556.
Doherty-Sneddon, G., Bruce, V. Bonner, L., Longbotham, S. and Doyle, C. (2002).
Development of Gaze Aversion as Disengagement from Visual Information.
Developmental Psychology, 38, 438-445.
Doherty-Sneddon, G. and Phelps, F.G. (in press). Gaze aversion: a solution to cognitive
or social difficulty? Memory and Cognition.
Glenberg, A.M., Schroeder, J.L, and Robertson, D.A. (1998). Averting the gaze
disengages the environment and facilitates remembering. Memory and Cognition,
26, 651-658.
Goldin-Meadow, S., Wein, D., and Chang, C. (1992). Assessing Knowledge Through
Gesture: Using Children's Hands to Read their Minds. Cognition and Instruction,
9, 201-219.
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Graham, J., Haidt, J., and Nosek, B. A. (2009). Liberals and conservatives rely on
different sets of moral foundations. Journal of Personality and Social Psychology,
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Kleinke, C.L. (1986). Gaze and eye contact: a research review. Psychol. Bull. 100, 78-
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Spellman, B. A., and Schnall, S. (2009). Embodied rationality. Queen’s Law Journal, 35,
117-164.
Trope, Y., and Liberman, N. (2010). Construal-level theory of psychological
distance. Psychological review, 117(2), 440.
107
General Conclusion
Aristotle’s definition of man as a rational animal has recently come under attack
from both Psychology and Neuroscience. Oppugning ancient rationalist philosophy that
emphasized the role of reason in moral judgment, recent work in Psychology and
Neuroscience highlights the role of emotion in moral judgment. However, no studies so
far have investigated how an observer’s trait level empathy and the moral transgressor’s
display of emotion may influence the observer’s moral judgment of real-life
transgressions. In my dissertation, I measured psychophysiological responses (heart rate
and skin conductance), brain activity and natural expressive behavior as observers made
moral judgments of real-life situations.
Overall, the findings described in the first chapter suggest that emotion displayed
by the transgressor differentially influences the observers’ moral judgment such that they
are more severe in judging the happy transgressor than in judging the sad transgressor,
and that this difference correlated with greater change in heart rate and skin conductance
responses to the happy versus the sad transgressor.
The second chapter examined the relationship between the observers’ moral
judgment of transgressions and their neural response. Observers who were influenced by
the transgressor’s emotion display were also more likely to recruit brain regions involved
in perspective taking and emotion processing during their moral judgment. This
corroborates the evidence that moral judgment requires the observers to engage in
perspective taking, and relate another’s situation to their own self and understand how
this changes their own emotion state and feelings. I also found that the activation pattern
of vmPFC, a brain region implicated in moral reasoning, differentiated the observers’
108
response to the transgressor’s happy, sad or no emotion display. Specifically, as also seen
in the behavioral and psychophysiological responses, activity change in the vmPFC was
the largest and fastest in response to the happy transgressor as compared to the sad
transgressor.
Chapter three investigated the neural networks in relation to moral judgment. The
role of the default mode network (DMN) in moral judgment has been previously
documented (Reniers et al., 2012; Laird et al., 2011; Shilbach et al., 2008). I found that
those observers whose moral judgment was influenced by the transgressor’s emotion
display were more likely to show stronger connectivity between the DMN and neural
regions involved in emotion processing and perspective taking. Further, stronger coupling
at rest between the DMN and the salience network (SN) was associated with faster
response times only when the transgressor displayed an emotion. This is in line with the
proposed role of the SN in orienting to an emotionally relevant stimulus and thereby
facilitating the switch to the DMN during moral judgment (Chiong et al., 2013).
Chapters four and five, briefly describe the association between observers’ trait
level empathy and influence of emotion on moral judgment, and their spontaneous
behavior during moral judgment. Observers who scored high on the trait empathy were
more severe in their judgment of the happy transgressor and less severe in their judgment
of the sad transgressor. Observers who judged the happy transgressor as displaying less
guilt also showed greater gaze aversion. Further, gaze aversion was associated slower
speech rate during judgment of happy, sad and neutral transgressors.
109
This study makes several contributions. First, the study is novel in its (i)
examination of moral judgment of real-life situations of the kind we encounter in
everyday life and doing so by using dynamic video-clips, (ii) investigation of the role of
the observers’ trait level empathy and the transgressors’ emotion display on moral
judgment. Second, the study demonstrated the advantages of integrating multiple
methods, leading to the first demonstration of how psychophysiological responses,
connectivity of neural networks and spontaneous natural behavior correlate with moral
judgment.
As the next steps, it would be interesting to isolate the precise point at which the
emotion influenced moral judgment of the observers (was it when the text was
presented/scenario was read, when the transgressor displayed the emotion). The use of
electroencephalography (EEG) could help define the temporal sequence of the influence
of emotion on moral judgment. Affective and cognitive processes change with age and a
longitudinal study could reveal how the influence of empathy and emotion may change
across the life span.
This thesis has some important implications. It contributes to the literature in both
moral psychology and neuroscience. By emphasizing the role of empathy and emotion,
this work has implications for education policy and decision making. For example,
understanding how our moral judgments are influenced by emotion and empathy might
help us determine the extent to which our judgments are perceptions of external situations
or projections of internal feeling states and tendencies. Further, this understanding will
help us maneuver social interactions, urging a more informed attempt to understand
cultures and social circumstances different from our own. These findings also emphasize
110
the concern to develop children’s impartiality by fostering empathy and greater
awareness of their emotion states and feelings.
111
References
Chiong, W., Wilson, S. M., D’Esposito, M., Kayser, A. S., Grossman, S. N., Poorzand,
P., ... and Rankin, K. P. (2013). The salience network causally influences default
mode network activity during moral reasoning. Brain, 136(6), 1929-1941.
Laird, A. R., Fox, P. M., Eickhoff, S. B., Turner, J. A., Ray, K. L., McKay, D. R., ... and
Fox, P. T. (2011). Behavioral interpretations of intrinsic connectivity
networks. Journal of cognitive neuroscience, 23(12), 4022-4037.
Reniers, R. L. E. P., Corcoran, R., Völlm, B. A., Mashru, A., Howard, R., and Liddle, P.
F. (2012). Moral decision-making, ToM, empathy and the default mode
network. Biological Psychology, 90(3), 202-210.
doi:http://dx.doi.org/10.1016/j.biopsycho.2012.03.009
Schilbach, L., Eickhoff, S. B., Rotarska-Jagiela, A., Fink, G. R., and Vogeley, K. (2008).
Minds at rest? Social cognition as the default mode of cognizing and its putative
relationship to the “default system” of the brain. Consciousness and cognition,
17(2), 457-467.
Trope, Y., and Liberman, N. (2010). Construal-level theory of psychological
distance. Psychological review, 117(2), 440.
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Singh, Vanessa
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Core Title
Role of emotion and empathy in moral judgments of real-life situations
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Doctor of Philosophy
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Psychology
Publication Date
12/09/2014
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