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The behavioral and neural correlates of music-evoked nostalgia across the lifespan

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The behavioral and neural correlates of music-evoked nostalgia across the lifespan

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Content THE BEHAVIORAL AND NEURAL CORRELATES OF MUSIC-EVOKED NOSTALGIA
ACROSS THE LIFESPAN
by
Sarah Louise Hennessy
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(PSYCHOLOGY)
AUGUST 2024
Copyright 2024 Sarah L. Hennessy

ii
Acknowledgements
Thank you to Assal, my advisor of nearly a decade. I am so grateful to have had you as a mentor
for so many years. I appreciate you pushing me to think critically, teaching me the value of
failure as well as success, and always supporting and cultivating my research and career
interests.
Thank you to Jonas, for your open office door and for answering every silly imaging question.
Thank you for allowing me to slowly infiltrate your group over the years.
To Antonio and Hanna: for cultivating a supportive interdisciplinary research environment.
Thank you for enabling projects like this to come to fruition.
Thank you to my committee members: Bruna Martins-Klein and Shri Narayanan, for your
conversations and research ideas on emotion, aging, and music. Thank you to Petr Janata for
inspiring this line of work, and for your thoughtful insights along the way.
Thank you to my family for supporting this journey from the beginning. To my sister, Anna, for
your positivity and cheerful anecdotes. To my parents, Deanie and Tom, for encouraging my love
of music when I was small, inspiring me to always be a better scientist, and for never failing to
pick up the phone.
To my Oxy friends, thank you for supporting me from near and far away. Jenna and Claire, thank
you for the real-life music-evoked nostalgia, for hyping up “PhD school”, and for the many

iii
sweet voice messages when I was hermitting. Jocelyn and Lily, thank you for the pep talks,
particularly during the earliest days.
Thank you to my USC friends and labmates. Amabel, Pragya, and Ian, for being an incredible
support system from Year 1 (and Amabel, for surviving the pandemic with me). Ellen, Xiao,
Anthony, Colin, Roshni, Helen, Chelsey, and Brock for making the BCI such a lively place in
which to work.
A huge thank you to my undergraduate research assistants; thank you for your hours of effort at
every stage of the research process and for your patience through all of these (and other)
projects. Thank you to Talia, Michael, and Caitlin for your work in coordinating.
For funding these studies, thank you to the Grammy Museum, USC Department of Psychology,
Society for Education of Music Psychology Research, Gold Family Fellowship, the USC
Hearing and Communication Neuroscience program, and the USC Brain and Creativity Institute.
Finally, to my fiancée, Vic: thank you for your unwavering support. Especially in this final year,
thank you for reminding me to be a human and for loving me even when I wasn’t. Thank you for
always celebrating the small wins. Thank you for the late-night vegan crunch wraps. Thank you
for leaving home for me.

iv
Table of Contents
Acknowledgements......................................................................................................................... ii
List of Tables.................................................................................................................................. vi
List of Figures..............................................................................................................................viii
Abstract........................................................................................................................................... x
Introduction..................................................................................................................................... 1
Chapter 1: Unique affective profile of music-evoked nostalgia: An extension and conceptual
replication of Barrett et al., 2010 .................................................................................................... 5
Abstract....................................................................................................................................... 5
Introduction................................................................................................................................. 6
Methods....................................................................................................................................... 9
Results....................................................................................................................................... 19
Discussion................................................................................................................................. 70
Chapter 2: Content of music-evoked nostalgic experiences......................................................... 84
Abstract..................................................................................................................................... 84
Introduction............................................................................................................................... 85
Methods..................................................................................................................................... 89
Results....................................................................................................................................... 98
Discussion............................................................................................................................... 120
Chapter 3: Neural correlates of music-evoked nostalgia in younger and older adults ............... 130
Abstract................................................................................................................................... 130
Introduction............................................................................................................................. 132
Methods................................................................................................................................... 140
Results..................................................................................................................................... 161
Discussion............................................................................................................................... 201
Chapter 4: “We read the news today, oh boy”: intersubject neural synchronization during
nostalgic music listening in older adults..................................................................................... 216
Abstract................................................................................................................................... 216
Introduction............................................................................................................................. 217
Methods................................................................................................................................... 220
Results..................................................................................................................................... 232

v
Discussion............................................................................................................................... 242
Chapter 5: Conclusion................................................................................................................. 247
References................................................................................................................................... 256
Appendices.................................................................................................................................. 278
Appendix A: Supplementary Material for Chapter 1.............................................................. 278
Appendix B: Supplementary Material for Chapter 2.............................................................. 285
Appendix C: Supplementary Material for Chapter 3.............................................................. 288

vi
List of Tables
Chapter 1
Chapter 1 Table 1. Demographic characteristics of the study sample .......................................... 11
Chapter 1 Table 2. Means and standard deviations of context-level measures between Nostalgia
and Control songs. ........................................................................................................................ 20
Chapter 1 Table 3. Results of multilevel models, Nostalgia rating between Nostalgia and Control
songs. ............................................................................................................................................ 23
Chapter 1 Table 4. Results of multilevel models, positive and negative valence rating between
Nostalgia and Control ................................................................................................................... 24
Chapter 1 Table 5. Results of multilevel models, mixed valence rating between Nostalgia and
Control songs. ............................................................................................................................... 25
Chapter 1 Table 6. Results of combined multilevel model, person-level measures predicting
nostalgia rating.............................................................................................................................. 28
Chapter 1 Table 7. Results of multilevel models, interactions of context-level and person-level
measures: negative valence ratings............................................................................................... 31
Chapter 1 Table 8. Results of multilevel models, interactions of context-level and person-level
measures: positive valence ratings................................................................................................ 39
Chapter 1 Table 9. Results of multilevel models, interactions of context-level and person-level
measures: mixed valence ratings (using the minimum function). ................................................ 46
Chapter 1 Table 10. Results of multilevel models, interactions of context-level and person-level
measures: high arousal ratings...................................................................................................... 55
Chapter 1 Table 11. Results of multilevel models, interactions of context-level and person-level
measures: low arousal ratings. ...................................................................................................... 62
Chapter 1 Table 12. Results of linear regressions, person-level measures to predict Trait
Nostalgia (SNS). ........................................................................................................................... 69
Chapter 2
Chapter 2 Table 1. Spotify features for nostalgic songs. ............................................................ 100
Chapter 2 Table 2. Salience of the self: coding categories, percentages of narratives, and category
examples. .................................................................................................................................... 105
Chapter 2 Table 3. Objects of nostalgia: coding categories, percentages of narratives, and
category examples....................................................................................................................... 107
Chapter 2 Table 4. Person Type: coding categories, percentages of narratives, and category
examples. .................................................................................................................................... 109
Chapter 2 Table 5. Celebration type: coding categories, percentages of narratives, and category
examples ..................................................................................................................................... 109
Chapter 2 Table 6. Loss type: coding categories, percentages of narratives, and category
examples. .....................................................................................................................................111
Chapter 2 Table 7. Redemption vs Contamination: coding categories, percentages of narratives,
and category examples................................................................................................................ 113
Chapter 2 Table 8. LIWC features and examples. ...................................................................... 117

vii
Chapter 3
Chapter 3 Table 1. Demographic characteristics of sample........................................................ 142
Chapter 3 Table 2. Center coordinates for spherical regions of interest for ROI and PPI analyses.
..................................................................................................................................................... 159
Chapter 3 Table 3. Means and standard deviations (SD) of computer-derived musical features for
Nostalgia, Familiar Control, and Unfamiliar Control Songs. ..................................................... 163
Chapter 3 Table 4. Means and standard deviations for felt nostalgia, valence, and arousal during
music listening. ........................................................................................................................... 166
Chapter 3 Table 5. Coordinates of peak clusters by region for the Nostalgia > Familiar Control
contrast, Older > Younger. .......................................................................................................... 173
Chapter 3 Table 6. Coordinates of peak clusters by region for the Nostalgia > Unfamiliar Control
contrast, across participants. ....................................................................................................... 176
Chapter 3 Table 7. Coordinates of peak clusters by region for the Nostalgia > Unfamiliar Control
contrast, Older > Younger. .......................................................................................................... 180
Chapter 3 Table 8. Coordinates of peak clusters by region for the MoCA regressor, Nostalgia >
Familiar Control contrast, Younger > Older. .............................................................................. 186
Chapter 3 Table 9. Coordinates of peak clusters by region for the Trait Nostalgia regressor,
Control > Rest contrast, Older > Younger................................................................................... 188
Chapter 3 Table 10. Coordinates of peak clusters by region for the valence regressors, Nostalgia
> Familiar Control contrast, Older > Younger............................................................................ 190
Chapter 3 Table 11. Coordinates of peak clusters by region for the PPI analyses...................... 195
Chapter 4
Chapter 4 Table 1. Participant demographic characteristics. ...................................................... 221
Chapter 4 Table 2. MIR features of musical stimulus played during scan. ................................ 226
Chapter 4 Table 3. Regions of interest, center coordinates, and meta-analyses from which ROIs
were defined................................................................................................................................ 231
Chapter 4 Table 4. Coordinates of significant clusters for whole brain results. ......................... 233
Chapter 4 Table 5. Intersubject correlation values within regions of interest............................. 237

viii
List of Figures
Chapter 1
Chapter 1 Figure 1. Context-level differences between nostalgia and control songs, with songs
clustered within participants. ........................................................................................................ 22
Chapter 1 Figure 2. Trait Nostalgia predicting nostalgia rating of songs across conditions. ....... 27
Chapter 1 Figure 3. Person-level predictors of negative felt valence in response to nostalgia and
control songs. ................................................................................................................................ 38
Chapter 1 Figure 4. Person-level predictors of positive felt valence in response to nostalgia and
control songs. ................................................................................................................................ 45
Chapter 1 Figure 5. Person-level predictors of mixed valence, using the minimum function,
during nostalgia or control music conditions................................................................................ 51
Chapter 1 Figure 6. Person-level predictors of felt high arousal during nostalgic and control
music conditions. .......................................................................................................................... 54
Chapter 1 Figure 7. Person-level predictors of felt low arousal during nostalgic and control music
conditions...................................................................................................................................... 61
Chapter 1 Figure 8. Predictors of Trait Nostalgia......................................................................... 68
Chapter 2
Chapter 2 Figure 1. Word clouds of nostalgia-evoking musical artists by 15-year generation. ... 99
Chapter 2 Figure 2. Song-specific age........................................................................................ 102
Chapter 2 Figure 3. Song-specific age (SSA) for participants’ parents, only in songs in which
participant’s SSA was less than zero........................................................................................... 103
Chapter 2 Figure 4. Salience of the self in music-evoked nostalgia content. ............................. 104
Chapter 2 Figure 5. Objects of music-evoked nostalgia content. ............................................... 106
Chapter 2 Figure 6. Types of celebration and loss in music-evoked nostalgia content. ............. 110
Chapter 2 Figure 7. Redemption and Contamination sequences in music-evoked nostalgia
content......................................................................................................................................... 112
Chapter 2 Figure 8. Most frequently used words in nostalgic content descriptions................... 114
Chapter 2 Figure 9. Summary variables from LIWC22 across nostalgic descriptions............... 115
Chapter 2 Figure 10. Top LIWC features present in memories across higher- (left panel) and
lower- (right panel) level categories. .......................................................................................... 118
Chapter 2 Figure 11. Personality predictors of negative emotion words in nostalgia content. .. 120
Chapter 3
Chapter 3 Figure 1. Control song selection model. .................................................................... 148
Chapter 3 Figure 2. fMRI task design (A), and example shuffling of song triplets within one run
(B). .............................................................................................................................................. 153
Chapter 3 Figure 3. Word clouds for nostalgic song artists in younger and older adults. .......... 162
Chapter 3 Figure 4. Spotify features between conditions........................................................... 165
Chapter 3 Figure 5. Felt nostalgia, valence, and arousal during Nostalgia, Familiar Control, and
Unfamiliar Control songs............................................................................................................ 168
Chapter 3 Figure 6. Cognitive Ability (MoCA) and Trait Nostalgia (SNS) between age groups.
..................................................................................................................................................... 169

ix
Chapter 3 Figure 7. Whole brain results for Nostalgia > Familiar Control contrast, across all
participants.................................................................................................................................. 172
Chapter 3 Figure 8. Whole brain results for Nostalgia > Familiar Control contrast, Older >
Younger adults (top) and bar plots of younger and older adults percent signal change from rest in
the Nostalgia and Familiar Control condition, within clusters significant in Nostalgia >Familiar
Control, Older > Younger contrast (bottom)............................................................................... 175
Chapter 3 Figure 9. Whole brain results for Nostalgia > Unfamiliar Control contrast. ............. 179
Chapter 3 Figure 10. Whole brain results for Nostalgia > Unfamiliar Control, Older > Younger
contrast (top). Bar plots of younger and older adults percent signal change from rest in the
Nostalgia and Unfamiliar Control condition, within clusters significant in Nostalgia > Unfamiliar
Control, Older > Younger contrast (bottom)............................................................................... 181
Chapter 3 Figure 11. Mean percent signal change from rest between conditions and age groups in
a priori-selected regions of interest............................................................................................. 184
Chapter 3 Figure 12. Whole brain results for Cognitive Ability regressor: Nostalgia > Familiar
Control contrast, Younger > Older adults (top) and line plots of younger and older adults percent
signal change from rest in the Nostalgia and Familiar Control conditions against cognitive ability
score, within clusters significant in Nostalgia > Familiar Control, Younger > Older contrast
(bottom)....................................................................................................................................... 187
Chapter 3 Figure 13. Whole brain results for Trait Nostalgia regressor: Familiar Control > Rest
contrast, Older > Younger adults (left) and line plots of younger and older adults percent signal
change from rest in the Familiar Control conditions against trait nostalgia score, within clusters
significant in Familiar Control > Rest, Older > Younger contrast (right)................................... 189
Chapter 3 Figure 14. Whole brain results for positive and negative valence regressors: Nostalgia
> Familiar Control contrast, all participants. .............................................................................. 192
Chapter 3 Figure 15. Whole brain results for positive and negative valence regressors: Nostalgia
> Familiar Control contrast, Older > Younger adults (top) and line plots of younger and older
adults percent signal change from rest in the Familiar Control conditions against valence scores,
within clusters significant in Nostalgia > Familiar Control, Older > Younger contrast (bottom)
..................................................................................................................................................... 194
Chapter 3 Figure 16. PPI results for Left and Right Posteromedial Cortex (PMC) seed, Nostalgia
>Familiar Control (top) and Familiar Control > Rest (bottom) contrasts................................... 197
Chapter 3 Figure 17. PPI results for Heschl’s gyrus seed in Nostalgia > Rest (top) and Familiar
Control > Rest (bottom).............................................................................................................. 199
Chapter 3 Figure 18. PPI results for Left Angular Gyrus seed, Nostalgia > Rest contrast......... 200
Chapter 3 Figure 19. General PPI results in Nostalgia and Familiar Control conditions........... 201
Chapter 4
Chapter 4 Figure 1. Significant whole-brain ISC map during music listening......................... . 236
Chapter 4 Figure 2. Mean correlation values across participants in regions of interest. ............ 238
Chapter 4 Figure 3. Distributions of state and trait nostalgia. .................................................... 239
Chapter 4 Figure 4. Correlations between ROI-based ISCs and State Nostalgia ratings........... 240
Chapter 4 Figure 5. Correlations between ROI-based ISCs and Trait Nostalgia ratings............ 241
Chapter 5
Chapter 5 Figure 1. SoundsLikeThis online music-matching tool ............................................. 251

x
Abstract
Nostalgia, a sentimental longing for the past, is a pancultural emotion experienced by individuals
across the lifespan. Music, particularly from adolescence and early adulthood, is a potent
nostalgia trigger. While previous research has documented nostalgia’s affective characteristics
and psychological functions, less is known about nostalgia as evoked by music, and very little is
known about nostalgia in older adults. Specifically, the affective profile, content, and neural basis
of music-evoked nostalgia across the lifespan are poorly understood. This dissertation
encompasses four chapters, with data collected from 805 participants, examining this concept. In
this work, I show that music-evoked nostalgia is a mixed, albeit primarily positive, emotion that
contains a delicate interplay of affect and personality (Chapter 1). I demonstrate that nostalgic
music is derived mainly from the teenage and early adulthood years of both the individual and
their primary childhood caretakers. I show evidence of social cross-generational transfer and that
the content of music-evoked nostalgic experiences is mainly positive, social, and self-salient
(Chapter 2). Finally, I demonstrate that music-evoked nostalgia is accompanied by widespread
neural activation and co-activation in the default mode network, reward regions, and medial
temporal lobe (Chapter 3) in younger and older adults. Lastly, I show that these neural regions
are recruited synchronously across older adult participants while listening to a full-length
nostalgic piece of music (Chapter 4). In sum, this dissertation contributes to the psychological
and neural understanding of music-evoked nostalgia across the lifespan.

1
Introduction
“In explaining the causes of Nostalgia, the reason to be considered of near and remote
sources, I shall enumerate unusual things… I consider the nearest to be the quite
continuous vibration of animal spirits through those fibers in the middle brain in which
impressed traces of the Fatherland still cling.”
-Johannes Hofer (1688), Medical Dissertation on Nostalgia, ch. 6.
The earliest conception of nostalgia can be traced to the late seventeenth century when a young
Johannes Hofer observed psychological distress in homesick Swiss mercenaries (Hofer, 1934
(translated from 1688)). Nostalgia is derived from the Greek words “nostos” (return) and “algos”
(pain). Hofer, in a dissertation of his own, depicts individuals suffering from this ‘cerebral
disease’ as having a collection of disturbing symptoms, including excessive weeping, yearning
for home, anxiety, and anorexia. He writes that remnants of the past can trigger this disease state,
particularly the sound of brass jewelry adorning the cows of the Swiss Alps. These acoustic
triggers send homesick youths into a feverish state of nostalgia, where the vibration of the
cowbells resonate traces of the disease through the “middle brain” (Hofer, 1934).
In the past 300 years, nostalgia has undergone a remarkable rebrand. Far from the
pathology and vein-relieving treatments of the 17th century, nostalgia today is conceptualized as
a self- and social emotion that serves many adaptive functions (Wildschut et al., 2006). Nostalgia
is defined as “a wistful or excessively sentimental yearning for a return to or of some past period
or irrecoverable condition, " eliciting primarily positive (Hepper et al., 2012; Leunissen et al.,
2021; Sedikides et al., 2015; Wildschut et al., 2006) and secondarily negative (Hepper et al.,
2012; Holak & Havlena, 1998; Turner & Stanley, 2021) feelings.

2
While Hofer’s disease model of nostalgia may not have endured, his conceptualization of
nostalgia as an auditorily triggered state remains true. Specifically, music is a potent nostalgia
trigger (Barrett et al., 2010), allowing listeners to be transported to the past along the contours of
harmonic structure and melodic continuity. Music-evoked autobiographical memories (MEAMs;
Janata, 2007) have been studied since the early 2000s, with investigations detailing the content
(Janata, 2007), specificity (Belfi et al., 2016), and neural correlates (Janata, 2009) of musicevoked autobiographical memories in young adults. While nostalgia is inherently
autobiographical (Wildschut et al., 2006), more work is needed to uncover music-evoked
nostalgia's psychological and neural underpinnings beyond autobiographical memory alone.
Moreover, there is very little work exploring music-evoked nostalgia in older adults.
Emotion (Martins et al., 2015; Mather, 2012; Mather & Carstensen, 2005) and memory (Allen et
al., 2018; Amer et al., 2022; Mather & Carstensen, 2005) processes change with age. Yet, even in
the presence of neurodegenerative disease, some music-evoked emotions and autobiographical
memories have been shown to be preserved (Baird et al., 2018; Cuddy et al., 2017; El Haj et al.,
2012; Reschke-Hernández et al., 2020). Music-based interventions for individuals with
Alzheimer’s Disease and Related Dementias are growing in popularity due to their potential as
non-pharmacological treatments for behavioral and psychological symptoms of dementia
(BPSD) (Davoodi et al., 2023; Kwak et al., 2020; McCreedy et al., 2019). Yet, the underlying
mechanism of this potential preservation is not well understood. Furthermore, there is limited
research outlining the fundamental psychological and neural basis of music-evoked nostalgia in
healthy older adults, making interpreting related clinical work challenging.
The overarching goal of this dissertation is to develop a working psychological and
neuroscientific understanding of music-evoked nostalgia across the lifespan. In the first two

3
chapters, I use behavioral methods to investigate the psychological and memory features of
music-evoked nostalgia in adult participants aged 18-82 from across the United States.
In Chapter 1, I characterize the affective profile of self-selected music-evoked nostalgia
as compared to non-nostalgic, musically matched, familiar music. I observe, in a sample of 582
participants, that music-evoked nostalgia is a mixed but mostly positive emotion and report
evidence for intricate relationships between subjective feeling and personality characteristics.
This chapter is now published in Emotion (Hennessy et al., 2024).
In Chapter 2, I describe the content of nostalgic experiences as evoked by self-selected
music in an analysis of 2,100 texts across 744 participants. I observe that the content of nostalgic
experiences reflects nostalgia's social and psychological functions; texts are highly self-relevant
and social, containing more positive than negative words.
In Chapters 3 and 4, I investigate the neural underpinnings of music-evoked nostalgia in a
sample of 57 healthy younger and older adults. In Chapter 3, I demonstrate that, across the
lifespan, music-evoked nostalgia engages distinct neural systems from musical familiarity. I
show nostalgia-related activation in the regions involved in self-referential processing (default
mode network), autobiographical memory (medial temporal lobe), reward (ventral tegmental
area, caudate), and emotion regulation (ACC, VMPFC, insula). In Chapter 4, I examine the
neural correlates of music-evoked nostalgia in a more naturalistic manner, using a full-length
musical stimulus. Older adults listening to “A Day in the Life” (The Beatles, 1967) show
significant neural synchrony in regions of the brain involved in auditory processing (temporal
lobe), motor activity (supplementary motor area), self-referential processing (posterior cingulate
cortex, precuneus), and reward (caudate).

4
Finally, Chapter 5 presents an overall conclusion, synthesizing findings from all four
studies. I discuss the implications of this work for the fields of emotion and aging and note
directions for future research.

5
Chapter 1: Unique affective profile of music-evoked nostalgia: An extension and conceptual
replication of Barrett et al., 2010
Abstract
Nostalgia is a mixed emotion, often evoked by music. This study sought to conceptually replicate
and extend Barrett et al. (2010)’s pioneering work exploring music-evoked nostalgia, where the
authors identified person- and context-level predictors of the experience of nostalgia in music. In
a sample of 582 adults across the United States, we identified a self-selected nostalgic and
musically matched non-nostalgic, familiar songs for each individual, using an online survey in
2021. Participants listened to music and indicated feelings of valence and arousal, followed by
assessments of affect (PANAS-SF) and personality (TIPI, BANPS, SNS). Nostalgic songs were
rated higher in valence and arousal than familiar, non-nostalgic control songs, and higher in
mixed valence in some metrics. Individuals with higher trait-level Trait Nostalgia reported higher
nostalgia ratings across nostalgic and control songs. Interactions between context- and personlevel factors indicated that, personality influenced the felt valence and arousal profile of musicevoked nostalgia, distinct from Familiar Control music. While some personality types found
nostalgic music to make them feel more aroused and positive (those high in Care, Trait
Nostalgia, Anger), others felt more negative while listening (those high in Sadness). Lastly, we
extend the personality profile of a highly nostalgic person; trait-level Trait Nostalgia was
associated with Care, Play, Agreeableness, Extraversion, and Neuroticism. We demonstrate
affective and person-level contributors to music-evoked nostalgia observed in Barrett et al.,
(2010) hold even when controlling for familiarity and musical features. We provide novel
insights on complex interactions supporting this emotion, in a larger and more diverse sample
with personalized stimuli.

6
Introduction
Nostalgia is a mixed emotion (Hepper et al., 2024; Holak & Havlena, 1998; Turner &
Stanley, 2021), eliciting primarily positive (Hepper et al., 2012; Leunissen et al., 2021; Sedikides
et al., 2015; Wildschut et al., 2006) and peripherally negative (Hepper et al., 2012; Holak &
Havlena, 1998; Turner & Stanley, 2021) feelings, and is often accompanied by an
autobiographical memory (Wildschut et al., 2006). While conceptualizations have varied across
the past three centuries (Batcho, 2013), nostalgia is defined today as “a wistful or excessively
sentimental yearning for a return to or of some past period or irrecoverable condition” (MerriamWebster, 2024) and “a feeling of pleasure and also slight sadness when you think about things
that happened in the past”(Cambridge Dictionary, 2024). Nostalgia may serve several adaptive
functions, including fostering a sense of meaning (Routledge et al., 2008, 2012), solidifying
identity (Sedikides, Wildschut, Gaertner, et al., 2008; Sedikides et al., 2016), buffering against
existential threats (Juhl et al., 2010) and counteracting loneliness (Abeyta et al., 2020; Zhou et
al., 2008). Nostalgia can be triggered by feeling states such as loneliness or sadness (Wildschut
et al., 2006), and by external stimuli such as smell (Matsunaga et al., 2011; Reid et al., 2015) or
music (Barrett et al., 2010; Gibbs & Egermann, 2021; Mehnert, 2018; Sedikides et al., 2022;
Sterenberg, 2018).
Music-evoked nostalgia is commonly reported by individuals across the age spectrum
(Hanson et al., 2022) and cultures (Hanson et al., 2022; Hepper et al., 2024; Saarikallio et al.,
2020). When participants are asked to indicate discrete emotions experienced when listening to
music, nostalgia is consistently top-ranked, surpassed only by general “liking” of a song
(Jakubowski & Ghosh, 2021). The affective signature of music-evoked nostalgia is mixed, yet
unevenly; music-evoked nostalgia is reported as more positive than negative (Barrett et al.,

7
2010). In everyday life, this affective experience may depend on the context in which nostalgia is
evoked (Barrett et al., 2010; Newman & Sachs, 2020) and the individual-level characteristics of
the listener (Barrett et al., 2010).
Barrett et al. (2010) in Emotion
Music-evoked nostalgia, including its content, triggers, and psychological effects, has
been explored in depth for the past decade. These investigations are largely rooted in the work of
Barrett and colleagues (2010), who published the first comprehensive investigation of musicevoked nostalgia in Emotion. The authors investigated factors that may contribute to the
nostalgic listening experience, laying out a heuristic model consisting of context-level (i.e., song
familiarity, experienced emotions, autobiographical memories) and person-level (i.e., personality
traits, Trait Nostalgia, mood state) factors. The primary aim was to identify to what extent
context-level factors, person-level factors, and their interactions, predicted the experience of
music-evoked nostalgia. Participants completed a battery of person-level measures, including
personality questionnaires, followed by listening to 30 fifteen-second musical clips and
indicating associated felt emotions and memories.
Results indicated that the magnitude of music-evoked nostalgia was most strongly
predicted by context-level factors (i.e., familiarity, memory association, valence), but was also
predicted by person-level measures (i.e., trait-level Trait Nostalgia). Interactions between
context- and person-level effects broadly indicated that Trait Nostalgia most consistently
interacted with context-level measures, amplifying observed context-level effects. Lastly, the
authors investigated the relationship between Trait Nostalgia and other measured personality
traits, observing that Trait Nostalgia was predicted by trait-level sadness and neuroticism. As a
whole, this study has laid the groundwork for understanding music-evoked nostalgia. Music-

8
evoked nostalgia is a mixed emotion, highly influenced by contextual factors. It is also tied to
person-level factors such as Trait Nostalgia, which may, in turn, be part of a complex profile of
personality. This influential paper has stood as the foundation for subsequent investigations on
music-evoked nostalgia over the past thirteen years.
Aims of the present study
The major aim of the present study is to explore whether the findings of Barrett et al.
(2010)’s Emotion paper would be replicated in a larger, more diverse sample. The current study
aimed to extend this work in three main ways: 1) broadening the sample, 2) using updated
scales, and 3) personalizing music. We expanded the sample of the previous work (N = 226
University of California, Davis psychology undergraduate students) by including a larger group
of participants (N = 582), recruited from across the United States, and spanning across a broader
range of ages and locations. We used several updated versions of scales, including the Brief
Affective Neuroscience Personality Scales (BANPS; Barrett et al., 2013) instead of the fulllength Affective Neuroscience Personality Scales (Davis et al., 2003). We additionally modified
the report of subjective feeling to allow for continuous, bilateral measures of affective arousal
and valence. Dimensional affect models have been shown to capture mixed feelings in musical
stimuli better than discrete measures (Eerola & Vuoskoski, 2011). We believe that these methods
also account for the limitation as discussed in Barrett et al. (2010), in which the intensity of felt
emotions was not measured while allowing for the capture of a more complex, less discrete,
affective response to characterize music-evoked nostalgia. Lastly, we personalized musical
stimuli for each participant. Barrett et al. (2010) utilized the Billboard Top 100 to select songs
that may or may not elicit nostalgia from participants. These methods may not capture the broad
and heterogeneous array of music preferences observed across individuals. With the obsoletion

9
of radio and the ubiquity of online streaming platforms today, individuals’ most nostalgic music
is likely outside the limits of most popular songs. We instead utilized a novel method of stimulus
selection, involving both self-report and a machine-learning algorithm (Greer et al., under
review), that ensures that a) all participants have a set of highly personalized, autobiographically
salient, nostalgic songs, and b) all participants also have a set of non-nostalgic pieces of music
that are still familiar. We additionally controlled for release date, and acoustic features to ensure
that each stimuli pair differed only on nostalgia. In this way, we seek to understand which person
and context-level features may differ between nostalgic songs and non-nostalgic, yet familiar,
musically-matched songs. We note that methodological differences deem this work a conceptual
(not direct) replication and extension. These differences are noted in the Methods section of this
report. We hypothesize that findings observed in Barrett et al. (2010) will be replicated in the
present investigation, even when controlling for musical features and song familiarity, and in a
larger and more diverse sample.
Methods
This study and all protocols were approved by the University of Southern California’s
Institutional Review Board (IRB), and all methods were carried out by the IRB's guidelines and
regulations. All data were collected and analyzed anonymously. The requirement to obtain
informed consent from all participants was waived by the IRB’s ethics committee due to the
anonymity of data collection. Participants read an informational page at the beginning of the
study, describing study procedures, risks, and benefits, and were instructed to leave the survey if
they did not wish to continue participation.
Transparency and Openness

10
We report how we determined our sample size, all data exclusions, all manipulations, and
all measures in the study below, and we follow the JARS (Kazak, 2018). All data and analysis
code are available at [peer review link:
https://osf.io/864nx/?view_only=9a646d6c406e4e1d9b2640e7bb73ad9b] (public link provided
upon acceptance). Data were analyzed and visualized using R, version 4.0.5 (R Core Team,
2021) and the packages ggplot (Wickham, 2016) and lme4 (Bates et al., 2015). This study’s
design and its analysis were not pre-registered.
Power analysis
To determine the minimum sample size needed to detect an estimated a small person and
context-level interaction effect in our planned multi-level models, we conducted a power analysis
with the following parameters using the R package simr (Green et al., 2023): Cohen’s f
2
=0.3,
alpha = 0.05, power ≥ 0.8. A minimum sample size of 550 participants was needed.
Participants
Participants were 582 adults residing in the United States (see Table 1 for demographic
characteristics). Participants were recruited through Prolific.co (Palan & Schitter, 2018) on
December 13, 2021. Prolific.co is an online research platform with over 100,000 participants
globally who are vetted for their reliability. Inclusion criteria were: 1) fluent in English 2)
residing in the United States 3) over the age of 18 years. Participants were screened so that an
equal number of males and females participated. After removing 14 participants for improper
responses (failed attention checks or completing the survey too quickly), 754 participants
remained. Participants who had zero appropriately matched Control songs (see Statistical
Analysis) were excluded (N = 172), leaving 582 participants included in the final analysis.

11
Table 1.
Demographic characteristics of study sample.
Age
Mean (SD) 33.19
(13.25)
Gender
Female (%) 48%
Male (%) 49%
Nonbinary (%) 3%
US Region of
Residence
West (%) 37%
Midwest (%) 18%
South (%) 29%
Northeast (%) 16%
Country of
Childhood
United States (%) 95%
Other (%) 5%
Chapter 1 Table 1

12
Procedure
Participants completed one online survey (~1 hour long), displayed via Qualtrics (Qualtrics,
2022). Participants were instructed to complete the survey in a quiet space with speakers or
headphones. Audio quality was tested at the beginning of the survey. The survey contained three
main phases:
1. Musical Selection.
a. Reporting three nostalgia-inducing songs.
b. Listening to each of the self-reported Nostalgia songs and up to four candidate
Control songs as identified by the Control Song Selection Model (see Appendix
A: Supplemental Methods).
2. Context-level measures
a. Appraising songs based on familiarity, felt nostalgia, felt valence, and felt arousal.
3. Person-level measures
a. Completing personality and demographic measures
Deviating from Barrett et al. (2010), we chose to place person-level measures at the end of the
online survey, rather than at the beginning. This selection was motivated by two factors: (1) Due
to the volume of songs and responses participants were asked to attend to during the music
selection and context-level sections, we wanted to maintain participant attention for these
sections by placing them earlier in the survey; and (2) We wanted to avoid any inadvertent
priming that may have occurred by prompting participants to think about self and personality that
may have later impacted feeling responses to the music. Additionally, after each Nostalgic song,
participants were asked to write a brief description of the memory that was evoked by the song.
Results from these data will be reported in a future report. After completion of the survey,

13
participants were thanked for their time and compensated, on average, $4.55 USD (at the 2021
Prolific minimum rate of $6.37 per hour).
Materials
Musical stimuli
At the beginning of the survey, participants were given a definition of nostalgia
(“sentimental longing for the past”) and of a nostalgia-evoking song (“a song that brings you
back to a pleasant moment or era of your life and evokes a strong memory”). Then, they were
asked to complete a comprehension check, in which they were asked to choose the definition of
“nostalgia” and of a “nostalgia-evoking song”, “as this study defines it” from a list of five,
randomly shuffled, options (see Appendix A: Supplemental Methods). Participants were only
allowed to continue once they had chosen the correct response. Then, participants were asked to
enter three personally nostalgia-inducing songs and their artists. These songs were fed into our
previously described (Greer et al., under review) Control Song Selection Model (see Appendix
A: Supplemental Methods), to identify Control songs that were musically similar, familiar, but
non-nostalgic to the individual participant. Each control song was musically matched to its
Nostalgia song seed based on computationally derived, expressed valence (within 0.15 out of 1
point) and energy (within 0.15 out of 1 point) and was released within five years of the seed
song. This matching procedure aimed to ensure that songs between conditions were matched for
acoustic features and familiarity, which may consequently control for enjoyment, as preference
for certain acoustic features (Rentfrow et al., 2011) and familiarity are associated with liking a
piece of music. Each participant had a final set of three nostalgic and up to three control songs,
which they listened to in a random order during the survey.

14
The survey and Control Song Selection model was created and presented using Qualtrics
(Qualtrics, 2022) and SpotifyAPI (Lamere, n.d.). JavaScript was used to embed the control song
selection tool into the back end of the Qualtrics survey. This script and a web-based version of
this tool are publicly available at [peer review link:
https://osf.io/864nx/?view_only=9a646d6c406e4e1d9b2640e7bb73ad9b] (public link provided
upon acceptance) and [webpage link available upon acceptance].
Context-level measures
After each Nostalgia and Control song, participants were asked to rate the musical clip
for familiarity (“Not at all familiar,” “Somewhat familiar,” “Very familiar”) and whether the song
made them feel on a scale from 1 (“Not nostalgic at all”) to 9 (“Extremely nostalgic”). Then,
participants were asked to rate each song for felt valence using a two-part Likert question (“Rate
how positive the emotion was that you FELT while listening to the song” and “Rate how
negative the emotion was that you FELT while listening to the song”) and arousal using a twopart Likert question (“How activated was the emotion that you FELT while listening to the song”
and “How deactivated was the emotion that you FELT while listening to the song”). “Activation”
was chosen to describe arousal to capture embodied feeling states, in which a highly arousing
emotion is associated with highly activated, strong bodily sensations and a less arousing emotion
is associated with deactivation and weaker bodily sensations (Nummenmaa et al., 2014). This
language was taken directly from the circumplex model (Posner et al., 2005), and has been
successfully used in other investigations involving self-report of emotional arousal (e.g., Presti et
al., 2022). Both sets of questions were on a scale of 0 to 10. As described above, level of felt
nostalgia was also obtained (on a 9-point scale, ranging from “not at all nostalgic” to “extremely
nostalgic”) after listening to each musical piece.

15
Person-level measures
After the musical stimulus presentation, each subject filled out several surveys related to
nostalgia, personality, and affect. Surveys were presented in random order but maintained the
original order of questions within each task.
To assess general mood state at the time of the survey (during the past week), we
administered the Positive and Negative Affect Schedule, Short Form (PANAS-SF; Watson et al.,
1988). The PANAS-SF consists of ten items assessing positive affect and ten items assessing
negative affect. Participants were asked to indicate to what extent they felt each emotion (e.g.,
“excited”, or “irritable”) in the past week on a 5-point scale ranging from “Very slightly or not at
all” to “extremely”. Values for positive and negative items were averaged to create one Positive
Affect and one Negative Affect score for each participant.
We administered the 7-item version of the Southampton Nostalgia Scale (SNS;
(Sedikides et al., 2015a), a measure frequently used to assess trait-level nostalgia (Barrett et al.,
2010; Routledge et al., 2008). This task asks participants to rate, on a 7-point scale ranging from
1 (“Not at all”) to 7 (“Very much”), their experience of nostalgia in daily life. Items include
questions related to nostalgia’s importance (“How significant is it for you to feel nostalgia?”) and
proneness (“How often do you feel nostalgia?”). Scores across all items are averaged (with one
backward-scored item), to create one Trait Nostalgia score for each participant.
To assess the five-factor model of personality, we administered the Ten-Item Personality
Inventory (TIPI) (Gosling et al., 2003). In this task, participants identify the extent to which they
view themselves across five dimensions (Openness, Conscientiousness, Extraversion,
Agreeableness, Stability (previously, Neuroticism) with ten items rated on a ten-point scale
ranging from “Disagree Strongly” to “Agree Strongly.” Each item includes a forward-scored and

16
a reverse-scored word pair for participants to relate to, such as “reserved, quiet” and
“extraverted, enthusiastic”. Scores for each of the five factors are averaged to create one score
for each factor for each participant.
We additionally administered the Brief-Affective Neuroscience Personality Scales
(Barrett et al., 2013). This scale assesses six affective neurobiological systems of Play, Seek,
Care, Fear, Anger, and Sadness. The scale has 33 items, in which participants are asked to
indicate how much they agree with each statement (e.g., “When I am frustrated, I usually get
angry.”) on a 5-point scale ranging from “Strongly Disagree” to “Strongly Agree.” Scores are
averaged within each of the six systems, resulting in six final scores for each participant.
Each subject also indicated their age, gender, and country of residence for the majority of
their childhood. Two attention checks were given throughout the survey, ensuring high-quality
results.
Statistical Analysis
For all analyses, only complete pairs of Nostalgia and Control songs were included;
inputted Nostalgia songs that did not have appropriately matched Control songs (i.e., the Control
Song Selection procedure failed for that song) were excluded. Participants in which no
appropriate Control songs were identified for any Nostalgia songs, were excluded. Statistical
analyses were performed in R version 4.0.5 (R Core Team, 2021) using RStudio. To compute
standard linear regressions, we used the lm function of base R. To compute mixed multi-level
models, we used the lmer function from lme4 (Bates et al., 2015). While Barrett et al. (2010)
used SAS with SAS PROC MIXED to perform mixed models, we contend that the lmer function
in R is computationally comparable (Bates, n.d.). Models were fit with restricted maximum
likelihood estimation and an unstructured variance/covariance structure, as in Barrett et al.

17
(2010). For all models, effect size for individual fixed effects was calculated using Cohen’s f
2
(Cohen, 1992).
Context-level measures
To explore differences in context-level variables (nostalgia rating, felt arousal, felt
valence, mixed valence) between the Nostalgia and Control conditions, we used mixed effects
multi-level regression models. Raw values were used for context-level measures in this analysis.
Mixed valence was calculated using two different functions: 1) the Griffin formula (Thompson et
al., 1995), calculated by capturing the intensity and similarity of both positive and negative
emotions ((Positive + Negative)/2 - |Positive - Negative|), 2) the Minimum Function (MIN)
(Schimmack, 2001), which is derived from the minimum of the positive and negative value,
corresponding to the intensity of the less-dominant emotion. Two measures of mixedness were
included to capture different aspects of mixed feeling; while Griffin is most sensitive to the
similarity of two feelings, the minimum function is most sensitive to the intensity of the
mixedness. In the multi-level models, songs were clustered within each participant, with a
random intercept included for participant ID. Bonferroni correction was used for these variables
(number of comparisons = 8). Notably, the analysis in the present study differs from Barrett et al.
(2010) in that we used Nostalgia here as a categorical variable with two levels (Nostalgia and
Control) rather than as a continuous variable.
Person-level measures
To assess the role of person-level measures on ratings of nostalgia, we mean-centered
person-level scores across participants. In this analysis, we treated nostalgia rating as a
continuous measure and ignored the categorical designation of Condition. We then fit multi-level
models, regressing nostalgia ratings onto the SNS, TIPI, BANPS, and PANAS-SF separately.

18
Lastly, as done by Barrett et al. (2010), we fit two combined models: the first, including SNS,
TIPI, and PANAS, and the second, including SNS, BANPS, and PANAS. TIPI and BANPS thus
were never included in the same model, as justified by Barrett et al. (2010), due to their high
intercorrelation (Davis et al., 2003). We additionally tested whether the TIPI was intercorrelated
with the BANPS in our sample with a series of Pearson correlations to confirm this choice (see
Appendix A: Figure S3). In all multi-level models, songs were clustered within participants and
a random intercept was included for participant ID.
Interaction of context- and person-level measures
To assess the interaction of context-level variables and person-level variables, we meancentered all person-level scores across participants, as done in Barrett et al. (2010). We did not
mean-center or standardize context-level variables within participants since some participants
only had one measurement per condition. Given that the valence and arousal measures were all
on the same scale, we decided this would additionally be the best choice for interpretability. We
then estimated multi-level models, predicting felt valence (positive, negative, mixed) and arousal
(high, low) in four separate models. First, we included the SNS, BANPS, and PANAS. Secondly,
we include the SNS, TIPI, and PANAS. For each model set, we used Bonferroni correction for
multiple comparisons (number of comparisons = 5). For each person-level measure, we included
an interaction term of Condition to assess whether person-level measures impacted the emotional
experience of music listening in the Nostalgia condition alone, or across all music. Songs were
clustered within participants and a random intercept was included for participant ID.
Trait Nostalgia and Personality
To assess the relationship between Trait Nostalgia (as measured with the SNS) and other
personality factors, we repeated Barrett et al. (2010)’s analysis of regressing the SNS on BANPS

19
dimensions and BFI dimensions in separate linear regression models. BANPS and BFI factors
were mean-centered and SNS was kept in raw form.
All data and analysis code are available at [peer review link:
https://osf.io/864nx/?view_only=9a646d6c406e4e1d9b2640e7bb73ad9b] (public link provided
upon acceptance).
Results
After removing songs that were not properly matched in the Control Song Selection
procedure, our final dataset contained 1,117 Nostalgic songs and 1,117 non-nostalgic Control
songs. Song pairs (Nostalgia and Control) were accurately matched across relevant musical
features, and a more in-depth analysis of musical features across songs and conditions in this
dataset can be found in Greer et al., (under review). See Appendix A: Table S1 for means and
standard deviations of computer-derived musical features for Nostalgia and Control songs, and
Appendix A: Figure S2 for a visualization of musical genre similarity across conditions.
Context-level differences between Nostalgia and Control songs
See Table 2 for means and standard deviations of context-level measures in each
condition. The intra-class correlation coefficient (ICC) for nostalgia rating was ~0, indicating
that nearly none of the variance for nostalgia ratings was at the individual participant level before
including additional variables into the model. Nostalgic songs were rated as significantly more
nostalgic than Control songs (β = 5.70, t(539.71) = 93.70, padjusted < 0.001, f
2 = 5.21) (see Table
3). For felt valence and arousal, the ICCs were 0.025, 0.16, 0.09, and 0.22, indicating that 0.25%,
16%, 9%, and 22% of the variance for positivity, negativity, high arousal, and low arousal were
at the individual participant level before including additional variables into the model. Nostalgia
songs were also rated as significantly more positive valenced (β = 3.94, t(1700) = 41.76, padjusted

20
< 0.001, f
2 = 0.61) and higher arousal (β = 3.89, t(1681) = 39.27, padjusted <0.001, f
2 = 0.49) than
Control songs (see Figure 1; Table 4 and 5).
Table 2.
Means and standard deviations of context-level measures between Nostalgia and Control songs.
Chapter 1 Table 2
Control Mean
(SD)
Nostalgia Mean
(SD)
Nostalgia
Nostalgia Rating 2.31
(1.16)
8.02
(1.33)
Valence
Positive Valence 4.28
(2.85)
8.22
(2.12)
Negative Valence 1.03
(1.94)
1.08
(1.98)
Mixed Valence (Griffin Formula) -1.39
(2.14)
-2.86
(2.81)
Mixed Valence (Minimum
Function)
0.63
(1.15)
0.89
(1.59)
Arousal
High Arousal 2.37
(2.47)
6.26
(3.05)

21
Low Arousal 2.50
(2.94)
2.58
(3.02)
Note. Valance and arousal metrics are on a scale of 0 to 10, where 10 indicates greater feelings of
positive valence, negative valence, high arousal, or low arousal.
For mixed valence, using the Griffin Formula, the ICCs were 0.18 and 0.21 for the
Griffin Formula and Minimum Function (MIN), respectively. Using the Griffin Formula, Control
songs were significantly more mixed in valence than Nostalgia songs (β = -1.47, t(524) = -13.34,
padjusted < 0.001, f
2 = 0.086). Conversely, using MIN, Nostalgia songs were significantly more
mixed in valence than Control songs (β = 0.26, t(522) = 4.03, padjusted < 0.001, f
2 = 0.009) See
Table 5 for detailed multi-level model results.

22
Figure 1
Context-level differences between nostalgia and control songs, with songs clustered within
participants.
Note. Bars represent standard errors. A. Positive and negative valence ratings for nostalgia and
control songs. Negative valence ratings are multiplied by -1 for visualization purposes, where
more negative values indicate greater feelings of negative valence. B. High and low arousal
ratings for nostalgia and control songs. Low arousal ratings are multiplied by -1 for
visualization purposes, where more negative values indicate greater feelings of low arousal. C.
Mixed emotion calculations for nostalgia and control songs using the Griffin Formula (upper
pane) and the Minimum Function (lower pane).
Chapter 1 Figure 1

23
Table 3.
Results of multilevel models, Nostalgia rating between Nostalgia and Control songs.
Chapter 1 Table 3
Note. SE = standard
error. p(adj) =p value,
adjusted using
Bonferroni’s correction.
Effect size is indicated
with Cohen’s f
2
.
Nostalgia Rating
Predictors β SE p (adj) 95% CI f
2
(Intercept) 2.33 0.04 <0.001 [2.25,
2.41]
condition [Nostalgia] 5.70 0.06 <0.001 [5.58,
5.82]
5.21
Random Effects
σ2 1.11
τ00 id 0.33
τ11 id.conditionNostalgia 0.93
ρ01 id -0.62
ICC 0.29
N id 582
Observations 2234
Marginal R2 / Conditional R2 0.839 / 0.885

24
Table 4.
Results of multilevel models, positive and negative valence rating between Nostalgia and Control
Chapter 1 Table 4
Positive Valence Negative Valence
Predictors β SE p(adj)
95%
CI
f
2
β SE p(adj)
95% CI f
2
(Intercept) 4.28 0.08 <0.00
1
[4.11,
4.44]
1.0
4
0.06 <0.001 [0.92,
1.16]
condition
[Nostalgia]
3.94 0.09 <0.00
1
[3.75,
4.12]
0.62 0.0
5
0.08 >1 [-0.10,
0.20]
~0.00
Random Effects
σ2 4.96 3.23
τ00 1.32 id 0.61 id
τ11
ρ01
ICC 0.21 0.16
N 582 id 582 id
Observations 2234 2234

25
Marginal R2 /
Conditional
R2
0.381 / 0.512 0.000 / 0.158
Table 3b 1
Note. SE = standard error. p(adj) =p value, adjusted using Bonferroni’s correction. Effect size is
indicated with Cohen’s f
2
.
Table 5.
Results of multilevel models, mixed valence rating between Nostalgia and Control songs.
s
Chapter 1 Table 5
Mixed Valence (Griffin) Mixed Valence (MIN)
Predictors β SE p(adj) 95% CI f
2 β SE p(adj) 95% CI f
2
(Intercept) -1.38 0.0
7
<0.001 [-1.52,-
1.23]
0.64 0.04 <0.001 [0.56,
0.72]
condition
[Nostalgia]
-1.47 0.1
1
<0.001 [-1.69, -
1.26]
0.086 0.26 0.06 <0.001 [0.13,
0.39]
0.009
Random Effects
σ2 3.93 1.14
τ00 1.08 id 0.36 id
τ11 2.74 id.conditionNostalgia 1.13 id.conditionNostalgia

26
ρ01 -0.07 id -0.18 id
ICC 0.37 0.42
N 582 id 582 id
Observations 2234 2234
Marginal R2 /
Conditional
R2
0.080 / 0.423 0.009 / 0.421
Note. SE = standard error. p(adj) =p value, adjusted using Bonferroni’s correction. Effect size is
indicated with Cohen’s f
2
.
Person-level measures
SNS score was a significant predictor of nostalgia rating (β = 0.20, t(2232) = 3.16, p <
0.01, f
2 = 0.004), such that participants with higher Trait Nostalgia found songs to be more
nostalgic (Figure 2). No factors of the TIPI, PANAS, or BANPS were significant predictors of
nostalgia rating (ps > 0.05) in individual models. In the combined model with TIPI, PANAS, and
SNS, only the SNS significantly predicted nostalgia ratings (β = 0.19, t(2225) = 2.77, p < 0.01,
f
2 = 0.003). In the combined model with BANPS, PANAS, and SNS again only the SNS
significantly predicted nostalgia ratings (β = 0.20, t(2224) = 2.70, p < 0.01, f
2 = 0.003). See
Table 4 for additional model details.

27
Figure 2
Trait Nostalgia predicting nostalgia rating of songs across conditions.
Chapter 1 Figure 2
Note. Trait Nostalgia is mean-centered. Shaded band represents 95% confidence interval.
Greater nostalgia scores indicate increased feelings of nostalgia while listening.

28
Table 6.
Results of combined multilevel model, person-level measures predicting nostalgia rating.
Chapter 1 Table 6
Nostalgia Rating
Predictors β SE p 95% CI f
2 β SE p 95% CI f
2
(Intercept) 5.17 0.07 <0.001 [5.04,
5.29]
5.17 0.07 <0.001 [2.88,
5.54]
SNS score 0.19 0.07 0.006 [0.06,
0.33]
0.003 0.20 0.07 0.007 [0.06,
0.35]
0.003
PANAS-SF pos 0.01 0.09 0.911 [-0.16,
0.18]
~0.00 0.03 0.09 0.694 [-0.02,
0.02]
~0.00
PANAS-SF neg -0.01 0.08 0.925 [-0.17,
0.15]
~0.00 -0.01 0.08 0.932 [-0.02,
0.02]
~0.00
TIPI extraversion 0.04 0.08 0.622 [-0.11,
0.19]
~0.00
TIPI agreeable 0.04 0.07 0.560 [-0.10,
0.18]
~0.00
TIPI conscientious 0.05 0.08 0.551 [-0.11,
0.20]
~0.00

29
TIPI stability -0.06 0.09 0.529 [-0.23,
0.12]
~0.00
TIPI openness -0.06 0.07 0.395 [-0.20,
0.08]
~0.00
BANPS play 0.01 0.08 0.934 [-0.21,
0.23]
~0.00
BANPS anger 0.03 0.07 0.718 [-0.15,
0.22]
~0.00
BANPS seek -0.03 0.07 0.699 [-0.26,
0.18]
~0.00
BANPS care -0.01 0.08 0.879 [-0.19,
0.16]
~0.00
BANPS fear -0.02 0.08 0.846 [-0.29,
0.24]
~0.00
BANPS sadness 0.02 0.09 0.828 [-0.17,
0.21]
~0.00
Random Effects
σ2 9.69 9.70
τ00 0.00 id 0.00 id

30
N 582 id 582 id
Observations 2234 2234
Marginal R2 /
Conditional R2
0.005 / NA 0.005 / NA
Note. SE = standard error. p(adj) =p value, adjusted using Bonferroni’s correction. Effect size is
indicated with Cohen’s f
2
.

31
Interactions of Context-Level and Person-Level Measures
Table 7.
Results of multilevel models, interactions of context-level and person-level measures: negative
valence ratings.
Chapter 1 Table 7
Negative Valence
Predictors β SE p(adj)
95%
CI
f
2
β SE p(adj)
95%
CI
f
2
(Intercept) 1.04 0.0
6
<0.001 [1.72,
1.84]
1.04 0.06 <0.001 [0.92,
1.17]
SNS score -0.08 0.0
7
>1 [-0.21,
0.05]
-
0.08
0.07 >1 -0.21,
0.06]
~0.00
condition
[Nostalgia]
0.05 0.0
8
>1 [-0.09,
0.20]
~0.00 0.04 0.08 >1 [-0.11,
0.19]
~0.00
PANAS-SF
pos
0.08 0.0
8
>1 [-0.09,
0.24]
~-0.00 0.12 0.08 0.78 [-0.04,
0.29]
~0.00
PANAS-SF
neg
0.26 0.0
8
0.005 [0.11,
0.42]
~-0.00 0.27 0.08 0.003 [0.17,
0.43]
~-0.00
TIPI
extraversion
0.16 0.0
7
0.155 [0.01,
0.30]
~-0.00

32
TIPI
agreeable
-0.21 0.0
7
0.01 [-0.34,
-0.08]
~-0.00
TIPI
conscientiou
s
0.04 0.0
7
>1 [-0.10,
0.18]
~-0.00
TIPI stability 0.22 0.0
8
0.05 [0.05,
0.39]
~-0.00
TIPI
openness
0.02 0.0
7
>1 [-0.12,
0.15]
~-0.00
SNS score *
condition
[Nostalgia]
0.03 0.0
8
>1 [-0.12,
0.19]
~0.00 -
0.01
0.08 >1 [-0.17,
0.15]
-0.00
condition
[Nostalgia] *
PANAS-SF
pos
-0.15 0.1
0
0.72 [-0.35,
0.05]
0.001 -
0.13
0.10 0.92 [-
0.33,-
0.43]
0.007
condition
[Nostalgia] *
PANAS-SF
neg
-0.18 0.0
9
0.305 [-0.36,
0.01]
0.01 -
0.25
0.10 0.05 [-0.43,
-0.06]
0.002

33
condition
[Nostalgia] *
TIPI
extraversion
-0.07 0.0
9
>1 [-0.24,
0.10]
~0.00
condition
[Nostalgia] *
TIPI
agreeable
0.17 0.0
8
0.205 [0.01,
0.33]
0.001
condition
[Nostalgia] *
TIPI
conscientiou
s
-0.21 0.0
9
0.09 [-0.38,
-0.04]
0.002
condition
[Nostalgia] *
TIPI stability
-0.25 0.1
0
0.08 [-0.45,
-0.05]
0.002
condition
[Nostalgia] *
TIPI
openness
0.10 0.0
8
0.217 [-0.06,
0.26]
0.001

34
BANPS play 0.03 0.08 >1 [-0.12,
0.18]
~0.00
BANPS
anger
-
0.03
0.07 >1 [-0.16,
0.10]
~-0.00
BANPS seek 0.05 0.07 >1 [-0.09,
0.19]
~0.00
BANPS care -
0.04
0.08 >1 [-0.18,
0.11]
~0.00
BANPS fear -
0.06
0.08 >1 [-0.22,
0.10]
~-0.00
BANPS
sadness
-
0.13
0.09 0.755 [0.31,
0.05]
~0.00
condition
[Nostalgia] *
BANPS play
0.06 0.10 >1 [-0.12,
0.25]
~0.00
condition
[Nostalgia] *
BANPS
anger
-
0.02
0.08 >1 [-0.18,
0.13]
~-0.00

35
condition
[Nostalgia] *
BANPS seek
-
0.02
0.08 >1 [-0.19,
-0.14]
~0.00
condition
[Nostalgia] *
BANPS care
0.03 0.09 >1 [-0.15,
0.20]
~0.00
condition
[Nostalgia] *
BANPS fear
0.04 0.10 >1 [-0.15,
0.23]
~-0.00
condition
[Nostalgia] *
BANPS
sadness
0.45 0.11 <0.001 [0.24,
0.66]
0.007
Random Effects
σ2 3.19 3.19
τ00 0.57 id 0.60 id
ICC 0.15 0.16
N 582 id 582 id
Observations 2234 2234

36
Marginal R2 /
Conditional
R2
0.028 / 0.176 0.022 / 0.176
Note. SE = standard error. p(adj) =p value, adjusted using Bonferroni’s correction. Effect size is
indicated with Cohen’s f
2
.
Valence
Negative Valence. First, we estimated a model that contained BANPS, PANAS-SF and
SNS, with condition as an interaction term for each predictor. We observed a significant main
effect of PANAS-SF Negative on felt negative valence (β = 0.27, t(1203) = 3.40, padjusted < 0.01,
f
2 = ~-0.00), where greater PANAS-SF Negative scores were associated with greater felt negative
valence across conditions. An interaction of PANAS-SF Negative and song Condition
approached significance (β = -0.25, t(1673) = -2.57, padjusted = 0.05, f
2 = 0.002), indicating that
this effect was more pronounced in the Control (β = 0.27) condition than the Nostalgia condition
(β = 0.02). There was a significant interaction effect of BANPS Sadness and song Condition on
felt negative valence (β = 0.45, t(1673) = 4.19, padjusted < 0.001, f
2 = 0.007). This interaction
indicated that, while in the Control condition, greater BANPS sadness scores were associated
with less negative valence (β = -0.13), in the Nostalgia condition it was associated with greater
negative valence (β = 0.32).
Then, we fit a model that contained TIPI, PANAS, and SNS. In this model, we observed a
main effect of PANAS-SF Negative (β = 0.26, t(546) = 3.36, padjusted < 0.01, f
2 =~-0.00), where

37
greater PANAS-SF Negative scores were associated with greater felt negative valence across
conditions. We observed a main effect of TIPI Agreeableness (β = -0.21, t(546) = -3.10, padjusted <
0.05, f
2 =~-0.00), where greater Agreeableness was associated with less felt negative valence
across conditions. See Figure 3 for plots of significant predictors of negative valence. See Table
7 for model details.

38
Figure 3
Person-level predictors of negative felt valence in response to nostalgia and control songs.
Note. Negative Affect and BANPS Sadness plots are depicted using the model with BANPS
predictors. TIPI Agreeableness plot is depicted using the model with TIPI predictors. Predictor
variables are mean-centered. Shaded bands represent 95% confidence intervals. Greater negative
valence scores indicate increased feelings of negative valence while listening.
Chapter 1 Figure 3

39
Table 8.
Results of multilevel models, interactions of context-level and person-level measures: positive
valence ratings.
Chapter 1 Table 8
Positive Valence
Predictors β SE p(adj)
95% CI f
2
β SE p(adj)
95%
CI
f
2
(Intercept) 4.29 0.0
8
<0.00
1
[4.13,
4.45]
4.28 0.0
8
<0.00
1
[4.13,
4.44]
SNS score 0.21 0.0
9
0.074 [0.04,
0.38]
~0.00 0.15 0.0
9
0.444 [-0.02,
0.33]
~-0.00
condition
[Nostalgia]
3.93 0.0
9
<0.00
1
[3.74,
4.11]
0.63 3.94 0.0
9
<0.00
1
[3.75,
4.12]
0.63
PANAS-SF
pos
0.27 0.1
1
0.07 [0.06,
0.48]
~-
0.00
0.16 0.1
1
0.73 [-0.05,
0.37]
~-0.00
PANAS-SF
neg
0.17 0.1
0
0.495 [-0.03,
0.36]
~0.00 0.23 0.1
0
0.134 [0.03,
0.43]
~-0.00
TIPI
extraversion
-
0.12
0.0
9
1 [-0.03,
0.36]
~0.00

40
TIPI
agreeable
0.22 0.0
9
0.07 [0.05,
0.39]
~0.00
TIPI
conscientiou
s
0.00 0.1
0
>1 [-0.18,
0.19]
~0.00
TIPI stability -
0.05
0.1
1
>1 [-0.27,
0.16]
~0.00
TIPI
openness
-
0.02
0.0
9
>1 [-0.19,
0.15]
~0.00
SNS score *
condition
[Nostalgia]
0.19 0.1
0
0.29 [-0.01,
0.38]
0.001 0.27 0.1
0
0.046 [0.07,
0.47]
0.002
condition
[Nostalgia] *
PANAS-SF
pos
-
0.13
0.1
3
>1 [-0.38,
0.12]
~-
0.00
-
0.07
0.1
3
>1 [-0.32,
0.17]
~-0.00
condition
[Nostalgia] *
PANAS-SF
neg
-
0.25
0.1
2
0.17 [-0.48, -
0.02]
0.001 -
0.22
0.1
2
0.31 [-0.46,
0.01]
0.001

41
condition
[Nostalgia] *
TIPI
extraversion
0.17 0.1
1
0.625 [-0.05,
0.38]
0.001
condition
[Nostalgia] *
TIPI
agreeable
-0.11 0.1
0
>1 [-0.31,
0.09]
~0.00
condition
[Nostalgia] *
TIPI
conscientiou
s
0.13 0.1
1
>1 [-0.08,
0.35]
~0.00
condition
[Nostalgia] *
TIPI stability
-
0.06
0.1
3
>1 [-0.31,
0.19]
~-
0.00
condition
[Nostalgia] *
TIPI
openness
-
0.06
0.1
0
>1 [-0.26,
0.14]
~-
0.00

42
BANPS play 0.23 0.1
0
0.124 [0.03
0.43]
~-0.00
BANPS
anger
-
0.14
0.0
9
0.54 [-0.31,
0.03]
~-.00
BANPS seek -
0.09
0.0
9
>1 [-0.27,
0.08]
~-0.00
BANPS care 0.05 0.1
0
>1 [-0.14,
0.24]
~-0.00
BANPS fear 0.01 0.1
0
>1 [-0.20,
0.21]
~-0.00
BANPS
sadness
-
0.03
0.1
2
>1 [-0.26,
0.19]
~-0.00
condition
[Nostalgia] *
BANPS play
-
0.20
0.1
2
0.445 [-0.43,
0.03]
0.001
condition
[Nostalgia] *
BANPS
anger
0.33 0.1
0
0.005 [0.13,
0.53]
0.004

43
condition
[Nostalgia] *
BANPS seek
0.12 0.1
0
>1 [-0.08,
0.32]
~0.00
condition
[Nostalgia] *
BANPS care
-
0.03
0.1
1
>1 [-0.25,
0.19]
~-0.00
condition
[Nostalgia] *
BANPS fear
-
0.14
0.1
2
>1 [-0.37,
0.10]
~-0.00
condition
[Nostalgia] *
BANPS
sadness
-
0.13
0.1
3
>1 [-0.39,
0.13]
~0.00
Random Effects
σ2 4.94 4.92
τ00 1.16 id 1.17 id
ICC 0.19 0.19
N 582 id 582 id
Observations 2234 2234

44
Marginal R2 /
Conditional
R2
0.403 / 0.517 0.405 / 0.519
Note. SE = standard error. p(adj) = p value, adjusted using Bonferroni’s correction. Effect size is
indicated with Cohen’s f
2
.
Positive Valence. In the model that included BANPS, PANAS-SF and SNS, we observed
an interaction effect between SNS score and song Condition (β = 0.27, t(1694) = 2.60, padjusted <
0.05, f
2 = 0.002), such that greater Trait Nostalgia scores were associated with greater felt
positive valence, particularly in the Nostalgia song condition. We additionally observed an
interaction between BANPS Anger and song Condition, (β = 0.33, t(1694) = 3.28, padjusted <
0.01, f
2 = 0.004), indicating that greater Anger scores were associated with less positive valence
in the Control condition (β = -0.14), and greater positive valence in the Nostalgia condition (β =
0.19).
In the model that included TIPI, PANAS, and SNS, there was a main effect of Condition
(β = 3.93, t(1696) = 41.69, padjusted < 0.001, f
2 = 0.63), such that positive valence was
significantly higher in the Nostalgia condition than the Control condition. See Figure 4. See
Table 8 for model details.
Figure 4
Person-level predictors of positive felt valence in response to nostalgia and control songs.

45
Chapter 1 Figure 4
Note. Both plots are depicted using the model that included BANPS. Predictors are meancentered. Shaded bands represent 95% confidence intervals. Greater positive valence scores
indicate increased feelings of positive valence while listening.

46
Table 9.
Results of multilevel models, interactions of context-level and person-level measures: mixed
valence ratings (using the minimum function).
Chapter 1 Table 9
Mixed Valence (MIN)
Predictors β SE p(adj)
95%
CI
f
2
β SE p(adj)
95%
CI
f
2
(Intercept) 0.64 0.05 <0.001 [0.55,
0.73]
0.64 0.05 <0.001 [0.55,
0.73]
SNS score -0.06 0.05 >1 [-0.15,
0.04]
~0.00 -0.06 0.05 >1 [-0.15,
0.04]
~0.00
condition
[Nostalgia]
0.26 0.05 <0.001 [0.16,
0.37]
0.009 0.26 0.05 <0.001 [0.16,
0.36]
0.009
PANAS-SF pos 0.03 0.06 >1 [-0.09,
0.15]
~0.00 0.07 0.06 >1 [-0.05,
0.19]
~0.00
PANAS-SF neg 0.25 0.06 <0.001 [0.14,
0.37]
~-0.00 0.24 0.06 <0.001 [0.12,
0.35]
~-0.00
TIPI extraversion 0.10 0.05 0.235 [0.00,
0.21]
~0.00

47
TIPI agreeable -0.05 0.05 >1 [-0.15,
0.04]
~0.00
TIPI conscientious 0.00 0.05 >1 [-0.10,
0.11]
~-0.00
TIPI stability 0.12 0.06 0.225 [0.00,
0.24]
~-0.00
TIPI openness 0.00 0.05 >1 [-0.10,
0.10]
~-0.00
SNS score *
condition
[Nostalgia]
0.06 0.05 >1 [-0.04,
0.17]
~0.00 0.03 0.06 >1 [-0.08,
0.14]
~0.00
condition
[Nostalgia] *
PANAS-SF pos
-0.06 0.07 >1 [-0.20,
0.07]
~0.00 -0.04 0.07 >1 [-0.17,
0.10]
~0.00
condition
[Nostalgia] *
PANAS-SF neg
-0.22 0.06 0.003 [-0.35,
-0.09]
0.004 -0.25 0.07 <0.001 [-0.38,
-0.12]
0.005
condition
[Nostalgia] *
TIPI extraversion
-0.03 0.06 >1 [-0.15,
0.08]
~0.00

48
condition
[Nostalgia] *
TIPI agreeable
0.02 0.06 >1 [-0.09,
0.13]
~0.00
condition
[Nostalgia] *
TIPI conscientious
-0.15 0.06 0.06 [-0.27,
-0.03]
0.002
condition
[Nostalgia] *
TIPI stability
-0.14 0.07 0.25 [-0.27,
-0.00]
0.001
condition
[Nostalgia] *
TIPI openness
0.06 0.06 >1 [-0.05,
0.17]
~-0.00
BANPS play 0.01 0.06 >1 [-0.10,
0.12]
~0.00
BANPS anger -0.02 0.05 >1 [-0.11,
0.08]
~0.00
BANPS seek 0.01 0.05 >1 [-0.09,
0.10]
~0.00
BANPS care 0.02 0.05 >1 [-0.09,
0.12]
~-0.00

49
BANPS fear -0.00 0.06 >1 [-0.12,
0.11]
~0.00
BANPS sadness -0.06 0.07 >1 [-0.19,
0.06]
~-0.00
condition
[Nostalgia] *
BANPS play
0.06 0.06 >1 [-0.07,
0.19]
~0.00
condition
[Nostalgia] *
BANPS anger
0.06 0.06 >1 [-0.05,
0.17]
~0.00
condition
[Nostalgia] *
BANPS seek
-0.02 0.06 >1 [-0.14,
0.09]
~0.00
condition
[Nostalgia] *
BANPS care
-0.05 0.06 >1 [-0.17,
0.07]
~0.00
condition
[Nostalgia] *
BANPS fear
-0.05 0.07 >1 [-0.17,
0.08]
~0.00

50
condition
[Nostalgia] *
BANPS sadness
0.31 0.07 <0.001 [0.17,
0.46]
0.006
Random Effects
σ2 1.49 1.49
τ00 0.40 id 0.41 id
ICC 0.21 0.22
N 582 id 582 id
Observations 2234 2234
Marginal R2 /
Conditional R2
0.034 / 0.236 0.032 / 0.241
Note. SE = standard error. p(adj) =p value, adjusted using Bonferroni’s correction. MIN refers to
mixed valence, using the minimum function (MIN). Effect size is indicated with Cohen’s f
2
.

Mixed Valence. For mixed valence, we opted to use MIN, given that there was a main
effect of song condition in the context-level models in the direction of our hypothesis, and the
fact that MIN is a better measure of uneven yet high-intensity mixed feeling. We estimated these
models assess whether person-level measures may moderate the relationship between nostalgia
and mixed valence via MIN. In the model including BANPS, PANAS-SF and SNS, we observed
a significant interaction effect of PANAS-SF Negative and song Condition (β = -0.25, t(1661) = -
3.90, padjusted < 0.001, f
2 = 0.005). This indicated that, in the Nostalgia condition, greater

51
PANAS-SF Negative scores were associated with less mixedness (β = -2.29), but in the Control
condition, greater PANAS-SF Negative scores were associated with more mixedness (β = 0.24).
We additionally observed an interaction of BANPS Sadness and song Condition (β = 0.31,
t(1661) = 4.30, padjusted < 0.001, f
2 = 0.006). In this case, greater Sadness scores were associated
with more mixedness in the Nostalgia condition (β = 0.25) and less mixedness in the Control
condition (β = -0.64).
In the model that included TIPI, PANAS, and SNS, we observed a significant interaction
effect of PANAS-SF Negative and song Condition (β = -0.22, t(1663) = -3.43, padjusted < 0.01, f
2
= 0.003), indicating that greater Negative scores were associated with more mixedness,
particularly in the Control Condition (β =0.25; Nostalgia β = 0.03). See Figure 5. See Table 9 for
model details.
Figure 5
Person-level predictors of mixed valence, using the minimum function, during nostalgia or
control music conditions.
Chapter 1 Figure 5

52
Note. Both plots are depicted using the model with BANPS. Predictors are mean-centered.
Shaded bands represent 95% confidence intervals.
Arousal
High Arousal. In the model including BANPS, PANAS-SF and SNS, we observed an
interaction effect between SNS score and song Condition (β = 0.37, t(1675) = 3.44, padjusted <
0.01, f
2 = 0.004), indicating that higher Trait Nostalgia was associated with higher arousal,
particularly in the Nostalgia condition (β = 0.49). We observed an interaction effect of PANASSF positive and song Condition, (β = -0.35, t(1675) = -2.63, padjusted < 0.05, f
2 = 0.002),
indicating that higher PANAS-SF positive scores were associated with higher arousal, but that
this relationship was stronger in the Control condition (β = 0.49) than the Nostalgia condition (β
= 0.14). We additionally observed an interaction effect between BANPS Care and song
Condition (β = 0.36, t(1675) = -3.03, padjusted < 0.05, f
2 = ~-0.00). This interaction indicated that
greater Care scores were associated with higher arousal in the Nostalgia condition (β = 0.22), but
lower arousal in the Control condition (β = -0.37). Lastly, there was an interaction effect
between BANPS Anger and song Condition (β = 0.41, t(1675) = 3.86, padjusted < 0.001, f
2 =
0.005), such that greater Anger predicted higher arousal in the Nostalgia condition (β = 0.27), but
lower arousal in the Control condition (β = -0.14).
In the model including TIPI, PANAS, and SNS, we observed an interaction effect
between SNS score and Condition (β = 0.35, t(1678) = 3.37, padjusted < 0.05, f
2 = 0.004),
indicating that higher Trait Nostalgia was associated with higher arousal, particularly in the
Nostalgia condition (β = 0.47). Lastly, we observed a main effect of PANAS-SF positive, (β =

53
0.47, t(1061) = 3.80, padjusted < 0.001, f
2 = ~0.00), indicating that greater positive scores were
associated with higher arousal. See Figure 6. See Table 10 for model details.

54
Figure 6
Person-level predictors of felt high arousal during nostalgic and control music conditions.
Chapter 1 Figure 6
Note. All plots are depicted using the model with BANPS. Predictors are mean-centered. Shaded
bands represent 95% confidence intervals. Greater high arousal scores indicate increased
feelings of high arousal while listening.

55
Table 10.
Results of multilevel models, interactions of context-level and person-level measures: high
arousal ratings.
Chapter 1 Table 10
High Arousal
Predictors β SE p(adj)
95%
CI
f
2
β SE p(adj)
95% CI f
2
(Intercept) 2.40 0.0
9
<0.00
1
[2.22,
2.58]
2.40 0.0
9
<0.001 [2.22,
2.58]
SNS score 0.12 0.1
0
>1 [-
0.07,
0.31]
~0.00 0.12 0.1
0
>1 [-0.08,
0.32]
~0.00
condition
[Nostalgia]
3.89 0.1
0
<0.00
1
[3.70,
4.09]
0.51 3.90 0.1
0
<0.001 [3.71,
4.09]
0.52
PANAS-SF
pos
0.47 0.1
2
<0.00
1
[0.23,
0.71]
~0.00 0.49 0.1
2
<0.001 [0.25,
0.73]
~-0.00
PANAS-SF
neg
0.16 0.1
2
0.875 [-
0.07,
0.38]
~-0.00 0.13 0.1
2
>1 [-0.10,
0.36]
~0.00

56
TIPI
extraversion
-0.15 0.1
1
0.810 [-
0.36,
0.06]
~0.00
TIPI
agreeable
0.03 0.1
0
>1 [-
0.16,
0.23]
~0.00
TIPI
conscientiou
s
-0.04 0.1
1
>1 [-
0.25,
0.17]
~0.00
TIPI
stability
0.03 0.1
3
>1 [-
0.21,
0.28]
~0.00
TIPI
openness
0.02 0.1
0
>1 [-
0.18,
0.22]
~0.00
SNS score *
condition
[Nostalgia]
0.35 0.1
0
0.01 [0.15,
0.55]
0.004 0.37 0.1
1
0.003 [0.16,
0.36]
0.004

57
condition
[Nostalgia] *
PANAS-SF
pos
-0.15 0.1
3
>1 [-
0.41,
0.11]
~0.00 -
0.34
0.1
3
0.043 [-0.60,
-0.09]
0.002
condition
[Nostalgia] *
PANAS-SF
neg
-0.14 0.1
2
>1 [-
0.38,
0.10]
~0.00 0.01 0.1
2
>1 [-0.24,
0.25]
~-0.00
condition
[Nostalgia] *
TIPI
extraversion
0.08 0.1
1
>1 [-
0.14,
-0.31]
~0.00
condition
[Nostalgia] *
TIPI
agreeable
0.05 0.1
1
>1 [-
0.16,
0.26]
~-0.00
condition
[Nostalgia] *
TIPI
conscientiou
s
0.05 0.1
2
>1 [-
0.17,
0.28]
~-0.00

58
condition
[Nostalgia] *
TIPI
stability
-0.07 0.1
3
>1 [-
0.32,
0.19]
~-0.00
condition
[Nostalgia] *
TIPI
openness
-0.07 0.1
1
>1 [-
0.28,
0.14]
~0.00
BANPS play 0.02 0.1
2
>1 [-0.20,
0.25]
~-0.00
BANPS
anger
-
0.14
0.1
0
0.865 [-0.33,
0.06]
~0.00
BANPS seek -
0.04
0.1
0
>1 [-0.24,
0.16]
~0.00
BANPS care -
0.14
0.1
1
>1 [-0.35,
0.08]
~-0.00
BANPS fear 0.02 0.1
2
>1 [-0.21,
0.25]
~-0.00
BANPS
sadness
0.10 0.1
3
>1 [-0.16,
0.36]
~0.00

59
condition
[Nostalgia] *
BANPS play
-
0.11
0.1
2
>1 [-0.36,
0.13]
~0.00
condition
[Nostalgia] *
BANPS
anger
0.41 0.1
0
<0.001 [0.20,
0.61]
0.005
condition
[Nostalgia] *
BANPS seek
0.08 0.1
1
>1 [-0.13,
0.29]
~0.00
condition
[Nostalgia] *
BANPS care
0.36 0.1
2
0.01 [0.13,
0.59]
0.003
condition
[Nostalgia] *
BANPS fear
-
0.31
0.1
3
0.065 [-0.56,
-0.07]
0.002
condition
[Nostalgia] *
BANPS
sadness
-
0.26
0.1
4
0.285 [-0.54,
0.01]
~0.00

60
Random Effects
σ2 5.44 5.35
τ00 2.00 id 2.02 id
ICC 0.27 0.27
N 582 id 582 id
Observations 2234 2234
Marginal R2
/ Conditional
R2
0.354 / 0.528 0.361 / 0.536
Note. SE = standard error. p(adj) =p value, adjusted using Bonferroni’s correction Effect size is
indicated with Cohen’s f
2
.
Low Arousal. No significant effects were found in the BANPS model. In the model
including TIPI, PANAS, and SNS, we observed a significant main effect of SNS on felt low
arousal (β = -0.30, t(552) = -2.85, padjusted < 0.05, f
2 = ~0.00), such that greater Trait Nostalgia
was associated with less low arousal across conditions. See Figure 7. See Table 11 for model
details.

61
Figure 7
Person-level predictors of felt low arousal during nostalgic and control music conditions.
Chapter 1 Figure 7
Note. Trait Nostalgia is mean-centered. Shaded bands represent 95% confidence intervals.
Greater low arousal scores indicate increased feelings of low arousal (or decreased arousal)
while listening.

62
Table 11
Results of multilevel models, interactions of context-level and person-level measures: low
arousal ratings.
Chapter 1 Table 11
Low Arousal
Predictors β SE p(adj) 95% CI f
2 β SE p(adj) 95% CI f
2
(Intercept) 2.46 0.1
0
<0.00
1
[2.27,
2.66]
2.47 0.1
0
<0.001 [2.28,
2.66]
SNS score -
0.30
0.1
0
0.022 [-0.50, -
0.09]
~0.00 -
0.28
0.1
1
0.054 [-0.49, -
0.07]
~-0.00
condition
[Nostalgia]
0.08 0.1
1
>1 [-0.14,
0.30]
~0.00 0.07 0.1
1
>1 [-0.15,
0.29]
~0.00
PANAS-SF
pos
-
0.03
0.1
3
>1 [-0.29,
0.23]
~0.00 -
0.07
0.1
3
>1 [-0.33,
0.19]
~0.00
PANAS-SF
neg
0.06 0.1
2
>1 [-0.18,
0.30]
~0.00 0.13 0.1
2
>1 [-0.11,
0.37]
~-0.00
TIPI
extraversion
0.07 0.1
1
>1 [-0.15,
0.30]
~0.00
TIPI
agreeable
0.06 0.1
1
>1 [-0.15,
0.27]
~0.00

63
TIPI
conscientiou
s
0.04 0.1
2
>1 [-0.18,
0.27]
~0.00
TIPI stability 0.00 0.1
3
>1 [-0.26,
0.26]
~0.00
TIPI
openness
0.06 0.1
1
>1 [-0.15,
0.27]
~0.00
SNS score *
condition
[Nostalgia]
0.13 0.1
2
>1 [-0.10,
0.36]
~0.00 0.10 0.1
2
>1 [-0.13,
0.34]
~0.00
condition
[Nostalgia] *
PANAS-SF
pos
-
0.12
0.1
5
>1 [-0.41,
0.18]
~0.00 0.03 0.1
5
>1 [-0.26,
0.32]
~0.00
condition
[Nostalgia] *
PANAS-SF
neg
-
0.01
0.1
4
>1 [-0.29,
0.26]
~0.00 -
0.16
0.1
4
>1 [-0.43,
0.12]
~0.00
condition
[Nostalgia] *
-
0.02
0.1
3
>1 [-0.27,
0.23]
~0.00

64
TIPI
extraversion
condition
[Nostalgia] *
TIPI
agreeable
-
0.13
0.1
2
>1 [-0.36,
0.11]
~0.00
condition
[Nostalgia] *
TIPI
conscientiou
s
-
0.01
0.1
3
>1 [-0.26,
0.25]
~-0.00
condition
[Nostalgia] *
TIPI stability
-
0.03
0.1
5
>1 [-0.32,
0.27]
~0.00
condition
[Nostalgia] *
TIPI
openness
0.17 0.1
2
>1 [-0.06,
0.41]
0.001
BANPS play -
0.06
0.1
2
>1 [-0.30,
0.18]
~-0.00

65
BANPS
anger
-
0.08
0.1
1
>1 [-0.28,
0.13]
~-0.00
BANPS seek 0.05 0.1
1
>1 [-0.16,
0.26]
~-0.00
BANPS care 0.20 0.1
2
0.45 [-0.03,
0.43]
~-0.00
BANPS fear -
0.25
0.1
3
0.26 [-0.49,
0.00]
~-0.00
BANPS
sadness
0.03 0.1
4
>1 [-0.24,
0.30]
~-0.00
condition
[Nostalgia] *
BANPS play
0.11 0.1
4
>1 [-0.16,
0.38]
~-0.00
condition
[Nostalgia] *
BANPS
anger
-
0.11
0.1
2
>1 [-0.34,
0.13]
~0.00
condition
[Nostalgia] *
BANPS seek
0.18 0.1
2
0.69 [-0.06,
0.42]
~0.00

66
condition
[Nostalgia] *
BANPS care
-
0.29
0.1
3
0.155 [-0.55, -
0.03]
0.002
condition
[Nostalgia] *
BANPS fear
0.21 0.1
4
0.685 [-0.07,
0.49]
0.001
condition
[Nostalgia] *
BANPS
sadness
0.28 0.1
6
0.365 [-0.03,
0.59]
0.001
Random Effects
σ2 5.44 6.86
τ00 2.00 id 1.91 id
ICC 0.27 0.22
N 582 id 582 id
Observations 2234 2234
Marginal R2
/ Conditional
R2
0.354 / 0.528 0.018 /
0.231
Note. SE = standard error. p(adj) =p value, adjusted using Bonferroni’s correction. Effect size is
indicated with Cohen’s f
2
.

67
Personality and Trait Nostalgia
We conducted two standard linear regressions. In the first, we regressed SNS onto the six
BANPS factors. Play (β = 0.32, t(575) = 7.35, p < 0.001, f
2 = 0.093), and Care (β = 0.12, t(575)
= 2.90, p < 0.01, f
2 = 0.014) were both significant positive predictors of Trait Nostalgia. See
Figure 8. See Table 12 for additional model details.

68
Figure 8
Predictors of Trait Nostalgia.
Chapter 1 Figure 8
Note. Plots are depicted controlling for other factors presented in the models. Predictors and
Trait Nostalgia are mean-centered for visualization purposes. Shaded bands represent 95%
confidence intervals. Plots depicted are partial correlations.
In the second model, we regressed SNS onto the five factors from the TIPI. This model
revealed that Extraversion (β = 0.20, t(576) = 4.78, p < 0.001, f
2 = 0.039), and Agreeableness (β
= 0.14, t(576) = 3.24, p < 0.01, f
2 = 0.018) were significant positive predictors of Trait
Nostalgia, such that participants who scored higher in Extraversion and Agreeableness were
higher in Trait Nostalgia. Stability (β = -0.11, t(576) = -2.49, p < 0.05, f
2 = 0.011), was a
significant negative predictor of Trait Nostalgia, such that participants who were higher in
Stability were lower in Trait Nostalgia.

69
Table 12.
Results of linear regressions, person-level measures to predict Trait Nostalgia (SNS).
Chapter 1 Table 12
SNS Score
Predictors β SE p 95% CI f
2 β SE p 95% CI f
2
(Intercept) 4.85 0.04 <0.001 [4.77,
4.92]
4.85 0.04 <0.001 [4.77,
4.92]
TIPI extraversion 0.20 0.04 <0.001 [0.12,
0.29]
0.039
TIPI agreeable 0.14 0.04 0.001 [0.05,
0.22]
0.018
TIPI conscientious 0.06 0.04 0.192 [-0.03,
0.14]
0.003
TIPI stability -0.11 0.05 0.013 [-0.20, -
0.02]
0.011
TIPI openness 0.06 0.04 0.165 [-0.02,
0.14]
0.003
BANPS play 0.32 0.04 <0.001 [0.24,
0.41]
0.093

70
BANPS anger -0.01 0.04 0.812 [-0.09,
0.07]
~-0.00
BANPS seek -0.03 0.04 0.416 [-0.11,
0.05]
0.001
BANPS care 0.12 0.04 0.004 [0.04,
0.21]
0.014
BANPS fear 0.00 0.05 0.945 [-0.09,
0.10]
~-0.00
BANPS sadness 0.09 0.05 0.067 [-0.01,
0.18]
0.006
Observations 582 582
R2 / R2 adjusted 0.082 / 0.074 0.158 / 0.149
Discussion
This study provides a conceptual replication and extension of Barrett et al. (2010)’s
investigation of music-evoked nostalgia. We collected responses to self-selected nostalgiaevoking songs and non-nostalgic control songs from 582 participants across the United States.
We explored context- and person-level predictors of music-evoked nostalgia and its associated
affective experience. We additionally examined the relationship between person-level variables
and trait-level Trait Nostalgia. Results demonstrated that both context-level factors, including the
felt arousal and valence of a song, and person-level factors, including trait-level Trait Nostalgia,

71
predicted music-evoked nostalgia. We observed that context- and person-level variables
interacted to produce differing affective experiences, including variation in how positively or
negatively nostalgia was felt. Overall, the results of the current study are largely consistent with
Barrett et al. (2010), conceptually replicating the findings in a larger and more diverse sample.
Results suggest that music-evoked nostalgia is a complex human emotion with a distinct
affective profile from non-nostalgic feelings elicited by familiar music, and a subjective
experience that may vary on individual characteristics of the listener.
Self-Report as a Stimulus Selection Method
To begin, we observed that self-report (i.e., directly asking participants to list three songs
that they knew would make them feel nostalgia) was a highly effective method of generating
personalized nostalgic stimuli for this study. All nostalgic songs were rated as very nostalgic,
with an average rating of 8.02 out of 9. This method achieved our goal of examining musicevoked nostalgia while accounting for the broad range of music-listening preferences and
experiences, through personalization. Our method of selecting paired control songs was also
effective; control songs were rated as significantly less nostalgic than self-selected nostalgia
songs (with an average rating of 2.3 out of 9). These songs were familiar, and matched based on
acoustic and musical features (Greer et al., under review), yet did not carry nostalgic value. We
contend that this method of stimulus selection may be an effective way for future work to study
music-evoked nostalgia or music-evoked autobiographical memories.
Nostalgia and Context-Level Constructs
In this study, we found that context-level constructs including valence and arousal
differed between nostalgia and control songs. While the methods used to assess this were slightly
different (i.e., predicting a binary vs a continuous measure of nostalgia, using discrete emotions

72
vs a bivalent scale), these results largely replicate Barrett et al. (2010)’s work. In our study,
participants felt more positive and higher energy while listening to nostalgic songs than while
listening to control songs. In relation to arousal, our findings replicate that of Barrett et al.
(2010), in which arousal positively predicted nostalgia rating, indicating that more nostalgic
songs were experienced as higher arousal. Here, we found that arousal was higher in nostalgic
than control songs, indicating that music that evokes nostalgia also evokes a high-energy profile
of feeling.
In Barrett et al., (2010), participants endorsed a greater number of different positive
emotions while listening to nostalgic music (e.g., love, longing, joy), compared to non-nostalgic,
non-autobiographical music. They additionally found that participants endorsed one particular
negative emotion (sadness) more while listening to nostalgic vs. non-nostalgic music. We did not
find in this study that negative valence was significantly higher for nostalgic vs. control songs.
This discrepancy might be due to the appraisals of specific emotions as they are reduced to
valence and arousal dimensions; perhaps some participants felt sad as a discrete emotion but did
not experience or report sadness as a negatively valenced emotion. This “pleasurable sadness” is
not an uncommon experience when listening to music (for review: Sachs et al., 2015) and may
account for this difference in findings. Additionally, because participants self-selected their
nostalgic pieces of music and the instructions included the word “pleasant”, it is likely that they
simply found the songs to be pleasant to listen to, and thus rated them as higher valenced.
Relatedly, we explored the concept of co-occurring positive and negative valence by
using two different metrics of mixed feelings. Barrett et al. (2010) observed that nostalgia ratings
were positively predicted by the incidence of co-occurring positive and negative discreet
emotions. Here, we observed that nostalgic songs evoked more mixed valence in comparison to

73
control songs according to MIN (Schimmack, 2001), but less mixed valence according to the
Griffin Formula (Thompson et al., 1995). Observed effects of mixedness in this study are
somewhat limited, however, due to the nature in which “nostalgia” and “nostalgia-evoking song”
were defined. The definition of “nostalgia-evoking song” contained connotations of positivity
which, we observed during pilot testing of the initial survey, was necessary to convey the task
instructions effectively. Without this additional clarification, participants often reported memoryevoking songs that they did not “long” for, but that were evocative of negative or traumatic
memories. However, this may have predisposed participants to report songs and therefore
subjective feeling responses that were more positive than mixed. Nonetheless, the inconsistency
in the two mixedness metrics suggests a need for further exploration. The Minimum Formula
provides an account of the intensity of a mixed emotion, where mixedness is calculated as the
degree to which a weaker affect conflicts with a stronger affect. The Griffin Formula provides an
account of the intensity in combination with the similarity of a mixed emotion, such that highly
intense mixed feelings are discounted by the degree of dissimilarity (the difference between
positive and negative). When the difference between positive and negative valence is 0 (a
perfectly even affect), the Griffin and MIN functions equate. Yet, in situations with less balance,
the Griffin mixedness is much lower than MIN mixedness, making the Griffin Formula less
sensitive to incidences of unevenly mixed emotions. In this study, nostalgic songs were rated as
highly positive, reaching nearly ceiling. Thus, in order to achieve a high level of mixedness,
according to the Griffin Formula, participants would have had to rate negative valence as or
nearly as intense as positive valence, which is extremely unlikely in situations of such high
intensity (Larsen et al., 2017). Instead, we observe that, in comparison to control songs, nostalgic
songs elicit higher intensity of co-occurring positive and negative feelings and accompanying

74
lower balance of such feelings. Here, the co-occurrence of positive and negative valence is
marked by a high intensity positive affect in combination with a low intensity (yet, non-zero)
negative affect. Taken together, the context-level findings of this study paint an affective profile
of the music-evoked nostalgic experience: a high arousal, unevenly mixed feeling that contains
more positive than negative valence.
Nostalgia and Person-Level Constructs
Among person-level variables, trait-level Trait Nostalgia was the only significant
predictor of nostalgia rating. Individuals who were more prone to experiencing nostalgia
experienced higher levels of nostalgia when listening to music. We did not replicate Barrett et al.
(2010)’s additional person-level findings, in which ANPS Play and PANAS negative affect
predicted nostalgia rating in individual models. This might be explained by the larger range of
variance in nostalgia ratings observed in Barrett et al. (2010). By design, our study had a smaller
range of nostalgia rating values, constituting the two song conditions. Yet, even in Barrett et al.
(2010), when considering all person-level factors in combined models, Trait Nostalgia was by far
the most consistent and influential factor contributing to the nostalgic experience. Our results
corroborate Barrett et al. (2010)’s, suggesting that Trait Nostalgia is the most significant personlevel variable contributing to the music-evoked nostalgic experience.
Interactions Between Context- and Person-Level Constructs
While Trait Nostalgia was the only person-level predictor of nostalgia rating, we did
observe the contribution of several other person-level variables when considered in combination
with context-level factors. We found support for the idea that the affective experience of musicevoked nostalgia may differ depending on a listener’s personality. We observed two general
groupings of context-level effects that appeared to be influenced by person-level constructs: 1)

75
an exaggeration of valence and arousal markers and 2) recategorization of the valence profile of
music-evoked nostalgia.
In the first, we observed that both individuals that were higher in BANPS anger and Trait
Nostalgia had an exaggeration of valence and arousal differences between nostalgia and control
songs as observed in context-level-only models. That is, anger and Trait Nostalgia were
associated with higher arousal and more positive valence in nostalgic music, whereas these
relationships were flatter (Trait Nostalgia) or negative (anger) for control music. The Trait
Nostalgia finding aligns with Barrett et al. (2010)’s finding, in which they observed tentative
support for Trait Nostalgia to increase the potency of context-level effects. These findings also
support the idea of a “Trait Nostalgia feedback loop” in which certain individuals experience
more reward from nostalgia, incentivizing them to engage in repeated nostalgia-seeking
behaviors. Interestingly, Trait Nostalgia did not predict the experience of mixed feelings while
listening. This may indicate that experiencing mixed feelings is not necessarily part of the
rewarding feedback loop that causes individuals to be drawn to nostalgic stimuli. The parallel
experience reported by individuals higher on BANPS anger may reflect an adaptive function of
music-evoked nostalgia, in which individuals more prone to feelings of anger may turn to
nostalgic music to effectively regulate their emotions. In line with the Regulatory Model of
Nostalgia (Wildschut & Sedikides, 2023), individuals with higher anger may experience greater
negative affect and then turn to nostalgia due to its highly positive and highly arousing feeling to
help balance and maintain homeostasis. Previous work has shown that music-evoked nostalgia
may help regulate emotions by allowing a listener to approach difficult situations (Gibbs &
Egermann, 2021).

76
We additionally observed increased arousal felt during nostalgia-evoking songs, with no
marked changes in valence. This affective profile was associated with increased BANPS care,
indicating that individuals higher in caring had an exaggerated experience of the arousal
increases associated with nostalgia in music. The BANPS care dimension is related to pro-social
behaviors (Barrett et al., 2013). This finding may suggest that individuals more attuned to these
behaviors may also experience the highly social emotion of nostalgia to be more emotionally
activated.
Conversely, we observed that some individuals experienced music-evoked nostalgia as an
opposite profile of valence; specifically, one that was more negative in nostalgic music than
control music. Individuals who scored higher on BANPS sadness reported greater feelings of
negative valence when listening to nostalgic songs, and less feelings of negative valence when
listening to control songs. This was accompanied by increased mixed valence. Thus, in contrast
to differences between nostalgia and control across participants, nostalgic songs for these
individuals still evoked more mixedness than control songs, but this mixedness was skewed
negatively instead of positively. Garrido (2018) observed a similar finding in their study
exploring the role of depression in the affective experience of nostalgia; while most participants
experienced positive affect, individuals with depressive tendencies (i.e., ruminative thinking)
experienced greater negative affect after listening to nostalgic music (Garrido, 2018). This may
indicate that participants who have greater sadness are utilizing nostalgic music to discharge
negative emotions (Saarikallio et al., 2013, 2020; Shifriss et al., 2015), intensifying the habitual
negative thought patterns experienced by these individuals. As argued by Garrido (2018), this
emphasizes that nostalgia may not always be an adaptive coping mechanism. Instead, the
adaptive function of nostalgia appears to vary across individual personalities. Future work

77
exploring the relationship between the affective profile of music-evoked nostalgia and functions
of music emotion regulation (e.g., using the Music Mood Regulation Scale; Saarikallio, 2012)
could elucidate this further.
Lastly, we observed several interactions involving PANAS-SF positive and negative
affect. We interpret PANAS-SF findings in this study with caution; given that participants
completed the PANAS-SF after listening to all songs in this study, we cannot interpret PANASSF findings in the same manner as done in Barrett et al. (2010). Their study used PANAS to
indicate the affective state of the participant before listening. Here, PANAS-SF scores can only
indicate affective state after listening to both nostalgic and non-nostalgic music. In this vein, our
findings indicate that individuals who found nostalgic music to be negative and control music to
be mixed with a negative skew also reported greater negative affect at the end of the study.
Additionally, individuals who found all music, but particularly control music, to be higher in
arousal reported greater positive affect at the end of the study. As with all observations in this
study, we cannot infer a causal relationship. Yet, these findings do align with previous work
indicating that the valence felt after a nostalgic experience is largely congruent with the valence
of the nostalgic trigger (Newman & Sachs, 2023). Overall, the interactive effects of person and
context-level factors on the nostalgic experience highlight the importance of individual
differences when considering the feeling experience of an emotion like nostalgia.
Predictors of Trait Nostalgia
Our final analysis investigated the accompanying person-level profile of Trait Nostalgia.
We replicated Barrett et al. (2010)’s finding that Stability (the direct inverse of Neuroticism) was
negatively correlated with Trait Nostalgia. This additionally aligns with findings from other
research groups published since Barrett et al. (2010)’s original paper (Newman et al., 2020;

78
Seehusen et al., 2013). This finding has sometimes been explained in terms of a ‘maladaptation
view’, a view proposed in the 17th century (for English translation: Anspach, 1934) in which
nostalgia is understood as a tendency to ruminate, retreating to the past to avoid the problems of
the present (e.g., Garrido, 2018; Kaplan, 1987; Zinchenko, 2011). However, this view has little
empirical support and is instead refuted by many pieces of counterevidence demonstrating the
adaptive functions of nostalgia (Frankenbach et al., 2021; Hepper et al., 2024; Umar Ismail et al.,
2020). Thus, we instead interpret this finding in terms of the Regulatory Model of Nostalgia
(Wildschut & Sedikides, 2023), in which discomforting situations elicit nostalgia and the
nostalgia alleviates discomfort (Wang, Sedikides, et al., 2023; Wang, Wildschut, et al., 2023).
Individuals high in neuroticism, by definition, have a greater tendency to experience negative
and discomforting feelings (Matthews et al., 1990; Rusting & Larsen, 1997). The fact that these
individuals are also high in Trait Nostalgia suggests that they may utilize nostalgia to cope with
these more frequent negative feelings, and thus report nostalgia as a more frequently occurring
and important emotion.
In addition to Stability, we observed that Trait Nostalgia was positively correlated with
Extraversion and Agreeableness. Previous work has shown that agreeableness, neuroticism, and
openness are correlated with state-level felt nostalgia in response to audiovisual stimuli (X.
Zhang et al., 2023), but to our knowledge, this is the first study to report this combination of
personality characteristics as correlated with trait-level Trait Nostalgia. Here, we build a more
complex personality profile of a highly nostalgic individual as one who is extraverted, agreeable,
yet unstable (“neurotic”). We posit that the moderating factor that likely connects these
personality traits is instead the social variable of Need to Belong (Allen et al., 2022; Leary et al.,
2013; Leary., 2007). Need to Belong refers to the desire for interpersonal attachments (Allen et

79
al., 2022; Leary., 2007). Extraversion, agreeableness, and neuroticism alone significantly predict
Need to Belong (Leary et al., 2013); that is, neurotic, agreeable extroverts appear to have deficits
in their sense of belonging. This deficit then is associated with loneliness (Mellor et al., 2008),
which triggers and is alleviated by nostalgia (Abeyta et al., 2020; Wildschut et al., 2006; Zhou et
al., 2021) due to nostalgia’s association with social experiences and memories (for review: Juhl
& Biskas, 2023; Sedikides & Wildschut, 2019). This is supported by Seehusen et al., (2013), in
which the connection between neuroticism and Trait Nostalgia was moderated entirely by Need
to Belong. Thus, the presence of extraversion and agreeableness as significant predictors of Trait
Nostalgia, in addition to neuroticism provide evidence for a regulatory (Wildschut & Sedikides,
2023) and social view of Trait Nostalgia (Seehusen et al., 2013).
Results from the BANPS measures additionally support this view. While Barrett et al.
(2010) found positive correlations between Trait Nostalgia and the ANPS dimensions of Seek
and Sadness, we found only positive correlations with Play and Care. These BANPS dimensions
map very closely, in both our sample and in Barrett et al. (2010) onto the Big Factor metrics of
neuroticism (correlated positively with Sadness), extraversion (correlated positively with Play,
Care, and Seek), and agreeableness (correlated positively with Play, Care, and Seek). This
provides further evidence for a combined regulatory and social model of Trait Nostalgia.
We posit that Barrett et al. (2010) may not have observed the same expanded profile of
personality characteristics associated with Trait Nostalgia due to their more limited sample of
northern California college students. College students likely provide less variance in personality
traits compared to the more diverse, national sample included in this investigation. Thus,
expanding the original work to include individuals across the age spectrum, who are more

80
racially and ethnically diverse, and from distributed geographical locations allows for a more
representative exploration of personality.
Limitations and Future Directions
We acknowledge several limitations of the present study. First, we note that this
investigation was not a direct replication of Barrett et al. (2010). We used updated versions of
several measures, administered person-level measures after music listening, rather than before
music listening, and used a categorical, rather than continuous measure, of nostalgia-evoking and
control songs, fundamentally altering the structure of many analyses. We additionally utilized
different definitions of several concepts (nostalgia-evoking song, arousal). We opted to use an
activation-based arousal definition to capture embodied feeling states (Nummenmaa et al., 2014),
but recognize that this change in definition makes comparisons between the present study and
Barrett et al. (2010)’s arousal findings challenging. We believe these differences, however, allow
for extended insights on music-evoked nostalgia, while remaining a conceptual, but not exact,
replication of Barrett et al. (2010). Future studies could consider adding even more latest
measures to highlight the nuances of nostalgic experience. For example, since the
conceptualization of the present study, (Newman et al., 2020) developed and validated the
Personality Inventory of Nostalgic Experiences (PINE) scale to assess trait and state nostalgic
experiences, which may provide additional information on person-level factors that influence
music-evoked nostalgia.
While the present study examined the differing affective experiences of music-evoked
nostalgia using valence and arousal dimensions, we note that doing so after listening to the song
poses some limits to the depth of our understanding of this emotion. Reflecting upon emotional
states after the emotional experience may impact the experience and reporting of such

81
experiences. Future work could implement continuous measures of nostalgia, valence, and
arousal, in which participants can give moment-to-moment reports of their feelings while
experiencing them. This method has been employed to investigate univariate music-evoked
emotions (Sachs et al., 2020; Vuoskoski et al., 2022), and could provide additional insight into
the dynamics of music-evoked nostalgia.
Additionally, we acknowledge the limitations of some of the measures utilized in this
investigation, namely the TIPI (Gosling et al., 2003). This scale is useful due to its short form,
allowing it to be included in long batteries of measures (as performed in this study). However, it
has been criticized due to its overall poor internal consistency (for review: Thørrisen & Sadeghi,
2023) and use of multi-descriptor items (containing two adjectives), which may increase
ambiguity for participants, and reduce reliability and validity (Herzberg & Brähler, 2006; Schult
et al., 2019). Due to the of this study for participants, we opted to use a short-form scale to assess
the Big Five personality traits, but future work may consider using a longer-form scale with
overall better psychometric properties.
Lastly, we note that, while reported findings are statistically significant, the effect sizes
observed in the present study were small (mostly < 0.2). This was particularly true for models
investigating the interaction between person and context-level variables, and thus such results
should be taken with caution. However, we believe that observed significant effects, while small,
still provide meaningful implications for our understanding of music-evoked nostalgia.
Constraints on Generality
The primary purpose of this study was to conceptually replicate and extend the work of
Barrett et al. (2010) in a larger and more diverse sample. While our sample consisted of a larger
number of individuals, recruited from across the United States, we note that we did not

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specifically sample representatively in terms of age, ethnic identity, or racial identity. Given this,
and that we additionally did not collect ethnic or racial identity information from participants,
our findings are not necessarily generalizable to the population of the United States. Future work
should employ a statistically representative sample to accomplish this goal. This is especially
relevant, given that much of the focus of current music-evoked emotion and memory research
today focuses on older adults and those with Alzheimer’s Disease and Related Dementias
(Edwards et al., 2023; Kaiser & Berntsen, 2023; Matziorinis & Koelsch, 2022). Secondly, our
study is limited in that it only sampled individuals from the United States, limiting
generalizability. Music-evoked nostalgia is a cross-cultural phenomenon (Hanson et al., 2022;
Saarikallio et al., 2020), and our understanding of this construct is largely limited to Western
cultures and music. Given the breadth of musical expressions (Athanasopoulos et al., 2021;
Jacoby et al., 2019; Mehr et al., 2019) and emotional constructs (Jackson et al., 2019; Mesquita
et al., 2016) across the globe, future work should explore this topic cross-culturally.
Conclusion
In this study, we conceptually replicated the majority of findings observed in Barrett et al.
(2010) in a larger sample of adults from across the United States, while controlling for musical
features and song familiarity. In addition to replicating Barrett et al. (2010), this study provides
several unique contributions to our understanding of music-evoked nostalgia. First, we
demonstrate that self-selected music is effective at inducing nostalgia and for which it is
relatively simple to obtain appropriate control stimuli. Second, we observe that the subjective
experience of music-evoked nostalgia is different from the subjective experience of musicallymatched, familiar pieces of music, and may vary depending on each individual's unique
combination of personality characteristics. Lastly, we expand the personality profile typically

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associated with high Trait Nostalgia to include a more complex portrayal of an individual
consistent with profiles of individuals seeking social belongingness.

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Chapter 2: Content of music-evoked nostalgic experiences
Abstract
Music is a potent trigger of nostalgia and autobiographical memories. While previous work has
characterized the content of nostalgia cued by text and autobiographical memories cued by
music, little work has examined the content of music-evoked nostalgia. Using self-selected
nostalgic music, we collected 2,100 nostalgic experiences from 744 U.S. participants aged 18-82
and assessed content themes using manual and automated coding methods (LIWC). We observed
that self-selected nostalgic songs were released during participants’ adolescence and early
adulthood (also known as the reminiscence bump) or during the same periods from their parents’
lives (cascading reminiscence bumps), replicating previous findings. We demonstrated that the
content of music-evoked nostalgia is predominantly self-focused, followed by social
relationships with significant others, friends, and parents. Participants used positive words more
frequently than negative words, and that word valence was predicted by the personality
dimension of sadness. Finally, we showed that the affective arc of nostalgic narratives was
mostly stable across participants, but conscientious individuals were more likely to describe a
negative-to-positive sequence rather than the opposite. This work illustrates that the content of
music-evoked nostalgic experiences largely mirrors the adaptive functions of nostalgia related to
strengthening identity and self-continuity and promoting feelings of social connectedness.

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Introduction
Nostalgia, a sentimental longing for the past, is characterized by positive affect (Hepper et al.,
2012; Leunissen et al., 2021; Sedikides et al., 2015; Wildschut et al., 2006) with negative
undertones (Hepper et al., 2012; Holak & Havlena, 1998; Turner & Stanley, 2021). Nostalgia
offers a window into one’s past self, pulling feelings of social connectedness (Cheung et al.,
2016; Juhl & Biskas, 2023) and personal identity (Sedikides et al., 2008, 2016) into the present
to help regulate negative mood (Abeyta et al., 2020) and loneliness (Abeyta et al., 2020; Zhou et
al., 2008). In this way, engaging with nostalgia provides a sense of social closeness, selfcontinuity, and meaning in life (Wildschut et al., 2006).
The content of nostalgic experiences primarily reflects the emotion’s adaptive
psychological functions. In the first investigation of nostalgia’s content, Wildschut and
colleagues (2006) qualitatively assessed the responses from 172 young adults in written response
to an event reflection task (ERT). Prompted to think about any event in a “nostalgic way,”
participants’ narratives featured the self as a protagonist, with events centering primarily around
people and momentous events. Additionally, the authors observed that nostalgic narratives
tended to contain an affective arc, in which a negative experience was often reframed or
transformed into a positive or triumphant one (redemption sequence) rather than the opposite
(contamination arc) (Wildschut et al., 2006). Nearly twenty years later, these themes were echoed
in a study that utilized an automated language analysis (meaning extraction method; Markowitz,
2021) on a collection of ~1800 texts in response to an ERT. The authors observed that nostalgic
texts contained more words related to social experiences (i.e., family, together) and more
reference to longing and places (Fetterman et al., 2024). Additional work has used similar
language models and dictionaries, like the Linguistic Inquiry Word Count (LIWC Pennebaker et

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al., 2001), to predict nostalgia from social media posts (Davalos et al., 2015). LIWC is a text
analysis software automatically categorizes words into linguistic and psychological dimensions
(Pennebaker et al., 2001). Chen and colleagues (2023) additionally developed a “nostalgia
dictionary” to identify nostalgia in various text contexts. Both studies found that these predictive
models relied on factor loadings related to affect, social connection, and time (Chen et al., 2023;
Davalos et al., 2015).
These studies provide a basis for understanding nostalgia as it is prompted by text-based
cues (e.g., “think about something in a nostalgic way”). However, nostalgia is often triggered
involuntarily by images (Oba et al., 2015), smells (Matsunaga et al., 2013), and music (Barrett et
al., 2010; Hennessy et al., in press). Music, in particular, offers a unique perspective on our
understanding of nostalgia due to its ubiquity in everyday life. Additionally, music might be a
more effective nostalgia trigger than text-based prompts. For example, Fetterman and colleagues
(2024) observed that writing broadly about nostalgia alone did not reliably induce nostalgic
feelings and that nostalgia was only reliably observed when writing about specific topics
(Fetterman et al., 2024). This suggests that writing as a prompt might inhibit feelings of nostalgia
– making experimental evidence using these methods to probe nostalgia’s benefits all the more
remarkable. Due to its rewarding and temporal nature, music might offer a medium through
which nostalgia can be experienced and later expressed with fewer inhibitions. Thus, the content
of music-evoked nostalgia might differ from nostalgia evoked by other triggers.
While previous work has examined the affective nature of music-evoked nostalgia
(Barrett et al., 2010; Hennessy et al., in press), its content is relatively underexplored. Previous
work has examined the content of a deeply intertwined concept: music-evoked autobiographical
memories (MEAMs; Janata et al., 2007). In one of the first studies on this topic, Janata and

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colleagues (2007) observed that the third most commonly endorsed feeling while listening to
popular music that evoked autobiographical memories was nostalgia. The authors observed,
similar to nostalgic content observed by Wildschut and colleagues (2006), MEAMs largely
centered around people as well as lifetime periods. Using LIWC (Pennebaker et al., 2001), the
authors observed that words containing social, leisure, and occupational words were most
represented in MEAMs. LIWC has been used to assess MEAMs in several related studies (Belfi
et al., 2020; Cuddy et al., 2017; Jakubowski & Ghosh, 2021; Sakka & Saarikallio, 2020; Yinger
& Springer, 2022; Zator & Katz, 2017). Similarly, MEAMs have been shown to contain words
related to social processing (Jakubowski, 2019; Jakubowski et al., 2020; Jakubowski & Ghosh,
2021; Sakka & Saarikallio, 2020; Yinger & Springer, 2022), positive affect (Cuddy et al., 2017;
Jakubowski & Ghosh, 2021; Yinger & Springer, 2022; Zator & Katz, 2017), and personal
activities and pronouns
The observed content of music-evoked autobiographical memories aligns with that of
nostalgia, paralleling nostalgia’s functions as a self-relevant and social emotion. Yet, there is very
little work specifically examining the content of music-evoked nostalgic experiences. The one
study that has examined the content of music-evoked nostalgia was conducted during the
COVID-19 pandemic (Gibbs & Egermann, 2021). The authors investigated the degree to which
individuals residing in the U.K. were using self-selected nostalgic music to emotionally regulate
during the challenges of lockdown and, in turn, qualitatively assessed the content of musicevoked nostalgic experiences. They observed that the content of music-evoked nostalgia during
lockdown was largely about periods of life and secondarily about people (Gibbs & Egermann,
2021). Content was categorized as primarily positive, secondarily mixed, and lastly negative
(Gibbs & Egermann, 2021).

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Yet, it is unclear whether the content of music-evoked nostalgia, outside of the context of
a pandemic lockdown, parallels that of nostalgia evoked by other cues or if it differs from musicevoked autobiographical memories. Additionally, the role of individual differences on nostalgic
content (i.e., personality) is unclear. While personality has previously been observed to impact
the likelihood and felt valence associated with music-evoked nostalgia (Barrett et al., 2010;
Hennessy et al., in press), we do not have a clear understanding of its relationship with the
content of nostalgic experience. The present study aims to address the following questions: 1)
What is the content of nostalgic experiences as evoked by self-selected music? 2) How does
personality affect the type of content, specifically the affective qualities, in music-evoked
nostalgic experiences? Secondarily, we will assess whether the presence of a “reminiscence
bump” is replicated in the context of self-selected nostalgic music. This refers to the
phenomenon in which people tend to report a greater number of more vivid autobiographical
memories from their adolescence and early adulthood (Rubin & Schulkind, 1997). This has been
replicated in numerous music-evoked autobiographical memory investigations (Bartlett &
Snelus, 1980; Jakubowski et al., 2020, 2021; Krumhansl & Zupnick, 2013). Additionally, we will
assess the presence of “cascading reminiscence bumps” (Krumhansl & Zupnick, 2013), in which
music-evoked memories are also observed for songs retrieved from a parental figure’s
reminiscence bump (Jakubowski et al., 2020; Krumhansl & Zupnick, 2013). To assess these
research questions, we use a self-report method of musical stimuli, asking each individual to
identify three nostalgic songs. We analyze themes of nostalgic content using both manual
methods, following coding guidelines outlined in Wildschut et al. (2006) and the LIWC-22
(Boyd et al., 2022; Pennebaker et al., 2001).

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We hypothesize that the content of music-evoked nostalgic experiences will broadly
reflect those of other nostalgic experiences from non-musical cues. Specifically, we predict that
the self will play a prominent role across narratives, and descriptions will be socially focused.
With regard to personality, we predict that trait nostalgia will be associated with greater use of
positive words and a greater shift from negative to positive valence within narratives
(redemption sequence; McAdams et al., 2001). Trait nostalgia has previously been shown to
relate to positive valence experienced from music-evoked nostalgia (Hennessy et al., in press),
and individuals high in trait nostalgia likely have greater practice using nostalgia as a regulatory
mechanism (Wildschut & Sedikides, 2023). Relatedly, we predict that individuals scoring higher
in personality dimensions related to negative affect (neuroticism and sadness) will show greater
use of negative words in nostalgic descriptions. While individuals higher in these dimensions
might be expected to show more sequences of contamination rather than redemption in nostalgic
narratives, the regulatory model of nostalgia (Wildschut & Sedikides, 2023) that nostalgia might
be particularly helpful for these individuals in reframing negative events. Thus, we may see more
redemption than contamination in individuals with higher neuroticism and sadness, despite
overall greater use of negative words.
Methods
This study and its protocols were approved by the University of Southern California Institutional
Review Board. Data were collected and analyzed anonymously. The requirement to obtain
informed consent from all participants was waived by the IRB’s ethics committee due to the
anonymity of data collection. Participants read an informational page at the beginning of the
study that described procedures, risks, and benefits, and were instructed to leave the survey if
wished to discontinue participation.

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Transparency and Openness
All data and analysis code are available at [osf link]. As the intention of this report was to
describe qualitative elements of music-evoked nostalgic experiences, a power analysis was not
conducted. This study’s design and its analysis were not pre-registered. Data were analyzed and
visualized using R, version 4.0.5 (R Core Team, 2021), and visualized with ggplot (Wickham,
2016).
Participants
Participants in this study are from the same pool as reported in Hennessy et al. (in press) and
Greer et al. (under review). Participants were recruited from Prolific.co. (Palan & Schitter, 2018)
on December 13, 2021. Prolic.co is an online research platform with over 100,000 participants
across the globe, vetted for reliability. We used the following inclusion criteria: 1) fluent in
English 2) residing in the United States 3) over the age of 18 years. Participants were screened so
that an equal number of males and females participated. We removed 14 participants for
improper responses (failed attention checks or completing the survey too quickly), and 10
participants for incomplete nostalgic content responses. The final sample included 744 adults
(48% female, 49% male, 3% nonbinary), aged 18 to 82 (mean age = 33.74 years, SD = 13.42
years).
Procedure
Procedures for this study are additionally reported in Hennessy et al., (in press) and Greer et al.,
(under review). Participants completed an online survey that took approximately one hour. The
survey was displayed via Qualtrics (Qualtrics, 2022) and participants were asked to take the

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survey from a quiet place with headphones or speakers. Audio quality was tested at the beginning
of participation. The survey contained three main phases:
1. Identification of nostalgic music
2. Listening to music
3. Reporting associated nostalgic content
4. Completing personality measures
Participants were also asked to report subjective feelings of valence and arousal after each song;
these results are reported in Hennessy et al. (in press). Participants were asked to report basic
demographic information (age, gender) at the end of the survey. Two attention checks were given
throughout the survey, ensuring high-quality results. After completing the survey, participants
were compensated, on average $4.55 USD, which was set at the 2021 Prolific minimum pay rate
of $6.57 per hour.
Materials
Nostalgic musical stimuli
Participants were asked to self-select three songs that they knew made them feel
nostalgic. Prior to this selection, participants were given a definition of nostalgia (“a sentimental
longing for the past” and of a nostalgia-evoking song (“a song that brings you back to a pleasant
moment or era of your life and evokes a strong memory”). Then, they were asked to complete a
comprehension check, in which they were asked to choose the definition of “nostalgia” and of a
“nostalgia-evoking song”, “as this study defines it” from a list of five, randomly shuffled,
options (see Hennessy et al., in press for further details). Participants could only proceed with the
survey once they had chosen the correct responses. Then, participants entered the titles and
artists of three songs that made them feel nostalgic. These songs and other, non-nostalgic songs

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(see Hennessy et al., in press) were then presented to participants in random order. Participants
listened to 30 seconds of each song, presented via an embedded Spotify player. The 30-second
clip was chosen by Spotify’s automatic preview by a privileged music segmentation algorithm.
Nostalgic content question
After each nostalgic song, participants were presented with an open-ended prompt:
“Please briefly describe the nostalgic experience that you occurred when you listened to the
song. What memory did it evoke? How did you feel about the memory?”. Participants could type
as much or as little as they liked in response to this prompt.
Personality Measures
Each subject filled out surveys related to personality, presented in random order but with intact
within-survey question order.
We administered the 7-item version of the Southampton Nostalgia Scale (SNS;
(Sedikides et al., 2015), a measure used to assess trait-level nostalgia (Barrett et al., 2010;
Routledge et al., 2008). Participants were asked to rate, on a scale ranging from 1 (“Not at all”)
to 7 (“Very much”), their experience of nostalgia, with questions related to nostalgia’s
importance (“How significant is it for you to feel nostalgia?”) and proneness (“How often do you
feel nostalgia?”). Scores across all items were averaged (with one backward-scored item), to
create a Trait Nostalgia score.
To assess the five-factor model of personality, we administered the Ten-Item Personality
Inventory (TIPI) (Gosling et al., 2003). In this task, participants indicated, on a ten-point scale
ranging from “Disagree Strongly” to “Agree Strongly”, the extent to which they viewed
themselves across five dimensions (Openness, Conscientiousness, Extraversion, Agreeableness,
Stability (previously, Neuroticism). Each personality dimension included a forward-scored and a

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reverse-scored word pair for participants to relate to, such as “reserved, quiet” and “extraverted,
enthusiastic”. Scores for each of the five factors were averaged to create one score for each
factor for each participant.
Lastly, we administered the Brief-Affective Neuroscience Personality Scales (Barrett et
al., 2013). Across 33 items, this scale assesses six affective neurobiological systems of Play,
Seek, Care, Fear, Anger, and Sadness. Participants were asked to indicate, on a 5-point scale
ranging from “Strongly Disagree” to “Strongly Agree”, how much they agree with each
statement (e.g., “When I am frustrated, I usually get angry.”). Scores were averaged within each
of the six systems, resulting in six final scores for each participant.
Analyses
Description of music
We retrieved musical features of each nostalgic song using Spotify’s Music Information
Retrieval API (Lamere, n.d.). This free integrates with Python using the spotipy module (Lamere,
n.d.) and retrieves musical features for any song that is available in Spotify’s library. We
extracted the following features using this tool: (1) energy: a measure of expressed perceptual
intensity (i.e., arousal) ranging from 0 to 1, (2) valence: a measure of conveyed positiveness
ranging from 0 to 1, (3) release date: year of the song’s initial release, (4) acousticness: a
confidence measure of whether a track is acoustic on a scale of 0 to 1, (5) danceability: the
degree to which a track is suitable for dancing, based on tempo, rhythmic stability, and beat
strength on a scale of 0 to 1, (6) instrumentalness: a confidence measure of whether a song
contains vocals on a scale of 0 to 1, (7) liveness: a confidence measure of whether a track is a
live audience recording from 0 to 1, (8) speechiness: the degree to which a track includes spoken
words from 0 to 1, (9) tempo: estimated tempo of a track in beat per minute, (10) loudness:

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loudness of a track in decibels, (11) popularity: the popularity of the artist of the track, between 0
and 100.
We assessed the presence of a nostalgic music-related reminiscence bump by calculating
“song-specific age” (SSA), the approximate age that a participant was when their nostalgic song
was released (release date – year of birth). We additionally calculated parental SSA. To do this,
we calculated parental SSA for songs in which participant SSA was less than 0 (i.e., the song was
released prior to the participant was born). We obtained descriptive statistics of these measures
across participants using R’s base package.
Human coding of nostalgic content
To assess the content of the music-evoked nostalgic experience in this study, we based our
protocol on the general categories identified by Wildschut and colleagues (2006). Free text
responses to the nostalgic content question were extracted and placed in a spreadsheet. Research
assistants were trained in coding responses based on the protocols below, in batches of 150
responses. Coding protocols were developed using Wildschut and colleagues (2006) in addition
to material from coding manuals obtained from the authors. Two research assistants
independently coded each response. For each batch, inter-rater reliability was calculated between
scorers as the percentage of exact matches present across all coding categories and responses. If
inter-rater reliability was less than 0.8 between scoring pairs, coders met to reconcile differences
with a third coder until a reliability of 0.8 or higher was achieved. The coding procedures are
described below, and the coding manual and categorization spreadsheets given to research
assistant during coding is available online at [osf.io/link]. After coding, results were descriptively
analyzed by comparing relative frequencies of coded responses within each category.

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Self salience. The salience of the self was identified in each response, coded as whether
the participant served as the 1) sole actor, 2) major role, 3) minor role) or 4) outside observer in
the response.
Object of nostalgia. The primary object of nostalgia was identified in each response,
coded as whether the nostalgic content was primarily about 1) persons, 2) momentous events, 3)
settings, 4) period in life, 5) animals, 6) tangibles, 7) past selves. In our coding procedures, we
allowed “tangibles” to include tangible objects and media pieces like albums and movies as long
as the response was about the physical object and not the experience of consuming the media
(i.e., “attending a concert” not be considered a “tangible” but “the cover art of the White Album”
would be). We further probed responses in which “person” was the primary object of nostalgia
to assess what the relationship between the person described and the participant. These were
coded as either 1) parent, 2) sibling, 3) other relative, 4) significant other, 5) friend, 6)
acquaintance, or 7) other. The “significant other” category was defined as any person for which
the participant had romantic or sexual feelings, which could include a current or past significant
other, an unrequited love, or undefined romantic or sexual relationship.
Celebration and Loss. We added two additional coding categories to Wildschut et al.
(2006)’s protocol: celebration and loss. While the “objects of nostalgia” category included
“momentous events”, we wanted to further probe the type of event and its valence. If a response
centered around a celebration, it was coded for whether it was 1) a minor celebration (e.g.,
expressing joy while enjoying a day outside), 2) a moderate celebration (e.g., passing an exam),
3) a major celebration (e.g., getting married). If a nostalgic response centered around a loss, it
was coded for whether it was 1) a minor loss, (e.g., losing a possession), 2) a moderate loss (e.g.,
parting from a loved one), 3) a major loss (e.g., a death of a loved one). Notably, responses were

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only counted as a loss if the actual content of the nostalgia contained a loss within the narrative,
not if they described a general sense of longing, as longing is a key component of nostalgia.
Redemption vs. Contamination. We assessed the degree to which narratives described
in nostalgic content responses followed either a redemption sequence, in which a negative scene
progresses to a positive scene, or a contamination sequence, in which a positive scene transforms
into a negative scene. Responses were coded as either 1) a redemption sequence, 2) a
contamination sequence, or 3) neither a redemption nor a contamination sequence.
Automated coding of nostalgic content
To complement the manual coding procedures of nostalgic content, we performed an automated
method of assessing the content of responses using the Linguistic Inquiry and Word Count
(LIWC-22) dictionaries (Boyd et al., 2022; Pennebaker et al., 2001). These dictionaries
categorize and count the frequency of words across a range of 100 topics and subtopics. We
extracted the feature values of LIWC category designations for each nostalgic content response.
First, we assessed responses at higher levels of the following LIWC categories: Drives,
Cognition, Affect, Social, Culture, Lifestyle, Physical, Perception. Then, we assessed responses
for all LIWC sub-categories, excluding items related to linguistic dimensions and grammar. Our
final list of lower-level dictionary words assessed was: i, we, youshehe, they, affiliation, achieve,
power, allnone, insight, cause, discrep, tentat, certitude, differ, memory, tone_pos,
tone_negemo_pos, emo_neg, swear, socbehav, socref, politic, ethnicity, tech, leisure, home, work,
money, relig, wellnessmental, substances, sexual, food, death, need, want, acquire, lack, fulfill,
fatigue, reward, risk, curiosity, allure, attention, motion, space, visual, auditory, feeling, time,
focuspast, focuspresent, focusfuture, netspeakassent, nonflu, filler. Higher-level LIWC category
values indicated a greater presence of words related to a particular category across the nostalgic

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narrative. We descriptively analyzed these categories by identifying the top ten categories present
at the higher and lower levels across narratives. One-sample multi-level models were performed
to test whether feature levels were significantly greater than zero. We used multi-level models to
account the clustered nature of the data, in which each participant had three nostalgic responses.
Responses were clustered within each participant and we included a random intercept of
participant ID. Multiple comparisons correction was performed using Bonferonni’s correction.
Then, to test our hypothesis that nostalgic content would contain more positive than
negative words and more past than present or future words, we fit separate multi-level regression
models, predicting feature value from the LIWC category designation. Effect size for these
models was calculated as Cohen’s f
2 (Cohen, 1992).
Impact of personality on nostalgic content
We assessed the impact of personality on presence of positive and negative words in nostalgic
responses, and presence of contamination vs. redemption sequences. We first fit two multi-level
linear regression models predicting positive and negative emotion (as index by LIWC) separately
from the five TIPI dimensions and SNS score. Then, we fit two multi-level linear regression
models using the lmer function from lme4 (Bates et al., 2015) predicting positive and negative
emotion (emo_pos and emo_neg) separately from the six BANPS dimensions and SNS score.
These items were not included in the same model due to evidence of their intercorrelation, and as
done in previous work (Barrett et al., 2010). Lastly, we fit two multi-level logistic regression
models using the glmer function from lme4 predicting the likelihood that a narrative would
contain a redemption vs. contamination sequence (among texts that contained either) first from
the TIPI and SNS score and secondly from the BANPS and SNS score. Effect size for these
models was calculated as Cohen’s f
2 (Cohen, 1992).

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Results
A total of 2,100 nostalgia-evoking songs and their associated content were retrieved from 744
participants. Texts had a mean of 40.39 words and a standard deviation of 32.24 words.
Description of self-selected nostalgic music
Using Spotify’s Music Information Retrieval API, we extracted acoustic features from each selfselected nostalgic song. Averaged Spotify feature values are presented in Table 1. Word clouds of
the top artists reported from self-selected nostalgia songs within each 15-year generation epoch
are shown in Figure 1. Only one participant was from the “Silent Generation” (born between
1928-1945), so they were excluded from this analysis. The top reported nostalgia song artist for
participants born between 1946 and 1964 (“Baby Boomers”, N = 56) was The Beatles. The top
reported nostalgia song artist for participants born between 1965 and 1980 (“Generation X”, N =
136) was also the Beatles. The top reported nostalgia song artist for participants born between
1981 and 1996 (“Millenials”, N = 294) was Britney Spears. The top reported nostalgia song
artist for participants born after 1996 (“Gen Z”, N = 257) was Taylor Swift.

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Figure 1.
Word clouds of nostalgia-evoking musical artists by 15-year generation.
Chapter 2 Figure 1
Note. Generation Z Year of Birth (YOB): 1997-2012 (N = 257). Millenials YOB: 1981 – 1996 (N
= 294). Generation X YOB: 1965-1980 (N = 136). Baby Boomers YOB: 1946- 1964 (N = 56).

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Table 1.
Spotify features for nostalgic songs.
Mean SD
acousticness 0.23 0.28
arousal 0.66 0.22
danceability 0.57 0.16
instrumentalness 0.06 0.19
liveness 0.18 0.15
loudness -7.61 3.76
popularity 62.03 17.71
release year 2000 15.33
speechiness 0.07 0.07
tempo 120.37 29.27
Chapter 2 Table 1
Note. Acousticness, danceability, instrumentalness, liveness, and speechiness are on a scale of 0
to 1, in which a score closer to one denotes more intensity of that feature. Valence and energy are
also on a scale of 0 to 1; however, 0.5 indicates the “neutral” midpoint, 1 is high or positive, and
0 is low or negative. Popularity is on a scale of 0 to 100, where higher values indicate more
streams on Spotify.
Reminiscence Bumps
Across participants, SSA was normally distributed, with a mean of 12.86 years, median of 14
years, and an interquartile range of 2 - 24 (see Figure 2). Given that a substantial portion of

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participants in the sample were still younger than the upper limit of previously observed
reminiscence bumps (age 29), we calculated SSA again, including only participants that were at
least 30 years of age (N = 397 participants, 1,191 songs). In this subset, we observed again a
normally distribution, with a mean of 20.32 years, median of 22 years, and an interquartile range
of 8.5– 33 (see Figure 2). Next, we assessed the presence of cascading reminiscence bumps
(Krumhansl & Zupnick, 2013), by assessing whether the release date of nostalgic songs released
before a participant’s birth corresponded to their parent’s reminiscence bump. Examining only
songs in which the SSA was less than zero (N = 321 participants, 493 songs), we assessed
participants nostalgic songs against parental SSA (Figure 3). We observed two normal
distributions; for mothers, the mean SSA was 16.76 (median = 18, IQR = 10-25), for fathers, the
mean SSA was 18.92 (median = 20, IQR = 11-27).

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Figure 2.
Song-specific age.
Chapter 2 Figure 2
Note: Left plot displays song-specific age across all participants (N = 744 participants, 2,100
songs). Right plot displays song-specific age for only participants aged 30 and older (N = 397
participants, 1,191 songs). Song-specific age was calculated as the approximate age that a
participant was when their self-selected nostalgic song was released (release date – year of birth).

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Figure 3.
Song-specific age (SSA) for participants’ parents, only in songs in which participant’s SSA was
less than zero.
Chapter 2 Figure 3
Note. N = 321 participants, 493 songs. SSA < 0 indicates that the participant’s nostalgic song
was released prior to the participant’s birth year. SSA was calculated for participants’ parents as
the approximate age of the parent when the participant’s nostalgic song was released (release
date – parent year of birth).

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Content of music-evoked nostalgic experience
Self-Salience
For the majority of memories, the self played either a “major role” (54.24% of memories), or the
entire role (“sole actor”; 42.81% of memories). In the remaining ~3% of cases, the self played
either a minor role (1%), or was an outside observer (1.95%). These proportions are displayed in
Figure 4 with examples for each category in Table 2.
Figure 4.
Salience of the self in music-evoked nostalgia content.
Chapter 2 Figure 4
Note. N = 744 participants, 2,100 songs.

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Table 2.
Salience of the self: coding categories, percentages of narratives, and category examples.
Category Percentage Example
sole actor 43.81 “This song brings me back to my time studying abroad in Italy
and walking the streets on my way to class every day”
major role 54.12 “..brings back memories of happiness and sadness, as I camped
with friends and this was our fun song”
minor role 1 “Just my parents jamming out as a kid and my first introduction to
the world of super rockstar air guitarist.”
outside
observer
1.95 “..seeing it on television I guess probably like in dance shows or
things like that …its not as if I was actually there but more like an
observer of other people having a good time listening to it”
Chapter 2 Table 2
Objects of Nostalgia
The objects of nostalgia observed across memories are reported in Figure 5 with examples for
each category in Table 3. The majority of memories centered around “periods in life” (40.1%)
and “persons” (37.05%). Momentous events accounted for 10.05% of memories, followed by
“settings” (4.19%), “past selves” (3.24%), “other” (3.14%), “tangible objects” (2.14%), and
“animals” (0.1%).

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Figure 5.
Objects of music-evoked nostalgia content.
Chapter 2 Figure 5
Note. Objects N = 744 participants, 2,100 songs. Persons N = 484 participants, 786 songs.

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Table 3.
Objects of nostalgia: coding categories, percentages of narratives, and category examples.
Category Percentage Example
periods in life 40.1 “it brought me back to the days of my senior year in high
school.”
persons 37.05 “This song reminded me again of my grandfather but more
specifically him sitting in his chair and singing it to me.”
momentous
events
10.05 “This song was played at my eighth grade graduation.”
settings 4.19 “This evokes memories of me staring out the window of
airplanes during takeoffs, and I feel happy about them.”
past selves 3.24 “….it has reminded me of a more innocent time in my life.”
other 3.14 “This is one of my most favorite songs. I can hear it all day
long.”
tangibles 2.14 “It reminds me of when I was a kid and got my first cassette
tape.”
animals 0.1 “Getting my dog! Very good, I love my dog”
Chapter 2 Table 3
We further assessed the type of person described in memories involving people (see Table 4,
Figure 5). Of person-focused memories, the majority of memories focused on a significant other
(32.32%), a friend(s) (29.13%), or a parent(s) (17.94%). Other relatives were the object of 8.14%

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of memories, siblings were the object of 6.62% of memories, followed by other relative(s)
(8.14%), other people (5.09%), and acquaintances (0.76%).
Table 4.
Person Type: coding categories, percentages of narratives, and category examples.
Category Percentage Example
significant
other
32.32 “This song reminds me of my first girlfriend. She dedicated this
song to me along with others. It made me feel special and cool
that someone would like me like that.”
friend 29.13 “It made me remember being at a club in England with my
friends and the whole club singing along when this song came
on. I love it.”
parent 17.94 “Driving back home from seeing a movie with my father a
couple years before he died in my first car, a maroon 79 Le
Baron, listening to an oldies station on it's AM radio with
glorious mono sound and we both started singing along to it”
other relative 8.14 “My aunt introduced me to Abba when I was little. When I
would visit her she would always play their albums for me, and
this was one of my favorites”
sibling 6.62 “It's my brother's favorite song, we listened to it many times all
the time, I think I'll never forget that song”
other 5.09 “When Princess Diana died . It brought back sad memories and
made me feel sad”

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acquaintance 0.76 “This song invoked a memory of a girl I use to know that was
killed in a pedestrian accident.”
Chapter 2 Table 4
Celebration and Loss
Celebrations occurred across object categories, and thus were not constrained to only memories
about “momentous events”. Celebrations were not observed in 88.17% of memories, thus only
the remaining 11.83% (249 memories) were coded for level (“major”, “moderate” or “minor”)
(see Table 5). Among memories that contained celebrations, the majority of celebrations were
minor (62.65%) (see Figure 6), followed by moderate (30.92%), and major (6.43%).
Table 5.
Celebration type: coding categories, percentages of narratives, and category examples.
Category Percentage Example
minor 62.65 “… I have many memories before races like 5k's, 1/2 marathons,
etc”
moderate 30.92 “The song reminds of every year around this time when we go down
to Memphis to my parents house to celebrate Christmas with my
family.”
major 6.43 “I remembered my wedding day 27 years ago. it brought smiles to
my face and love in my heart”
Chapter 2 Table 5

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Figure 6.
Types of celebration and loss in music-evoked nostalgia content.
Chapter 2 Figure 6
Note. Celebration plot shows only memories in which a celebration was described (11.83% of all
memories). Loss plot shows only memories in which a loss was described (7.08% of all
memories; N = 199 participants, 249 songs).
Loss occurred across object categories, and thus were not constrained to only memories
about “momentous events”. Losses were not observed in 93.92% of memories, thus only the
remaining 7.08% (128 memories) were coded for level (“major”, “moderate” or “minor”) (see
Table 6; Figure 6). Among memories that contained loss, minor (39.84%) and major (35.92%)
losses were observed in similar proportions, followed by moderate (24.22%) losses.

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Table 6.
Loss type: coding categories, percentages of narratives, and category examples.
Contamination vs. Redemption
Memories contained either a redemption or contamination sequence in only 9.29% of cases (195
memories). Of those memories, contamination sequences occurred in 61.5% of cases and
redemption cases in 38.5% (see Figure 7 and Table 7).
Category Percentage Example
minor 39.84 “ It reminded me of working out in the gym after I had a dance injury
and was trying to make it back to the stage”
major 35.94 “ This is a song that makes me feel sad immediately. I listened to it a
lot at a particular time in my life, my early 20's when I was
processing the long sickness and death of my mother and the end of
a serious relationship”
moderate 24.22 “ I got cheated on and I wanted to just hide.”
Chapter 2 Table 6

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Figure 7.
Redemption and Contamination sequences in music-evoked nostalgia content.
Chapter 2 Figure 7
Note. N = 744 participants, 2,100 songs.

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Table 7.
Redemption vs Contamination: coding categories, percentages of narratives, and category
examples.
Category Percentage Example
neither 90.71 “The memory of sitting under a tree in the front yard of my
house and listening to music on a portable disc player. I feel
positive about the memory.”
contamination 5.71 “This was supposed to be my wedding song. it was the song
that represented us. I can see us laying there listening to the
song imagining our wedding. Now i feel sad crying because it
never happened.”
redemption 3.57 “This song reminded me of memories when i was going
through a breakup, my boyfriend had cheated on me with my
bestfriend, and i was struggling very bad. This song reminded
me i was a fighter and wasnt gonna give up, i was gonna work
harder, like the lyrics say. It gave me strength. It made me feel
sad, but it also made me feel strong, powerful.”
Chapter 2 Table 7
LIWC Features
On average, 95.42% of words were able to be captured by LIWC22 in a given nostalgic
description. A word frequency wordcloud, excluding the words “song” , “memor*”, and
“remind*”, is presented in Figure 8. Summary variables indicated that, on average, 79.29% of
text were perceived as honest and genuine (Authentic), 70.3% of text contained positive or
emotional tone (Tone), 48% of text contained words related to logical or formal thinking

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(Analytical), and 20.1% of text contained words related to leadership or status (Clout) (see
Figure 9).
Figure 8.
Most frequently used words in nostalgic content descriptions.
Chapter 2 Figure 8
Note. Wordcloud displays top 300 words, excluding “song” and “memory”. Larger font size
indicates greater representation across the dataset.

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Figure 9.
Summary variables from LIWC22 across nostalgic descriptions.
Chapter 2 Figure 9
Note. Authentic = percentage of text perceived as honesty, genuineness. Tone = degree of
positive (negative) tone. Analytic = metric of logical, formal thinking. Clout = language of
leadership, status.
LIWC features were assessed at higher and lower levels separately (see Table 8).
Table 8.
LIWC features and examples.
Higher-level
category
Lower-level dictionary
word
Mean percentage
of text
Example target words
Affect 6.82 good, well, new, love
Positive Emotion 3.01 good, love, happy, hope

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Negative Emotion 0.84 bad, hate, hurt, tired
Positive Tone 5.35 good, well, new, love
Cognition 12.56 is, was, but, are
Causation 1.99 how, because, make,
why
Defferentiation 1.36 but, not, if, or
Insight 3.36 know, how, think, feel
Memory 3.54 remember, forget,
remind, forgot
Culture 0.39 car, united states,
govern*, phone
Drives 3.79 we, our, work, us
Affiliation 3.09 we, our, us, help
Lifestyle 8.04 work, home, school,
working
Leisure 5.16 game*, fun, play, party*
Work 2.11 work, school, working,
class
Perception 13.80 in, out, up, there
Auditory 4.10 sound*, heard, hear,
music
Feeling 1.68 feel, hard, cool, felt
Motion 1.99 go, come, went, came

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Space 5.53 in, out, up, there
Physical 0.73 medic*, food*, patients,
eye*
Social 7.43 you, we, he, she
Social behavior 2.25 said, love, say, care
Social referents 4.76 you, we, he, she
Other
Allure 7.08 have, like, out, know
1st person singular 10.15 I, me, my, myself
Past focus 6.86 was, had, were, been
Present Focus 2.80 is, are, I’m, can
Future Focus 0.45 will, going to, have to,
may
Time 5.73 when, now, then, day
Chapter 2 Table 8
Note. Features are shown for the top 20 most represented features across texts, and for features
for which we had specific hypotheses (positive emotion, time focus).
In higher-level categories (excluding Lingustic and Function words), Perception words were the
most represented in memories, followed by Cognition Lifestyle, Social, and Affect, (see Figure
10, left panel). One-sample models indicated that all categories were significantly greater than
zero (all p_corrected < 0.001); see Appendix B: Table S1. The greatest t values were observed for
Perception (t = 82.43), and Cognition (t = 73.53).

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Figure 10.
Top LIWC features present in memories across higher- (left panel) and lower- (right panel) level
categories.
Chapter 2 Figure 10
Note. In the left panel, all higher-level features are represented. In the right panel, only the top 20
lower-level features are represented. Colors on the righthand panel correspond to the higher-level
feature colors, except for those in orange (1st person singular, Allure,, etc.). For example, space is
a lower-level feature of Perception, so they are both represented in pink.
In lower-level dictionary words, the top represented dictionary word was 1st Person
Singular, followed by Allure, Past Focus, Time, and Space. One-sample t-tests indicated that all
dictionary words were significantly greater than zero (all p_corrected < 0.001); see Appendix B:
Appendix B: Table S1. T-values reflected the order of representation, in which the greatestrepresented dictionary words had the greatest t-values (e.g., 1st person singular (t = 64.04), Time
(t = 54.30), Past Focus (t = 49.71), Space (t = 45.82).

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We then tested our hypotheses that there would be a greater instance of positive emotion
words than negative emotion words, and greater instance of past words than present or future
words across memories. Positive emotion words were significantly more represented than
negative emotion words (β = 2.18, t(3456) = 24.06, p < 0.001, f
2 = 0.12). For time focused words,
there was a significant effect of past focus (β = 4.06, t(5581) = 36.98, p < 0.001, f
2 = 0.55), in
which past-focused words (6.7%) were significantly more represented than present-focused
(2.8%) or future-focused (0.45%) words and present-focused words were significantly more
represented than future-focused words.
Personality predictors of positive and negative emotion words
In the model with BANPS, the Sadness dimension of BANPS significantly predicted the use of
negative emotion words in memories (β = 0.22, t(724) = 3.26, p < 0.01, f
2 = 0.01), such that
individuals higher on the Sadness dimension had more frequent use of negative emotion words
(see Figure 11). In the model with TIPI, no personality dimensions were significant predictors of
negative emotion. No personality dimensions in either model predicted negative emotion.

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Figure 11.
Personality predictors of negative emotion words in nostalgia content.
Chapter 2 Figure 11
Note. Negative words are indexed via LIWC2022. Error bands represent 95% confidence
intervals.
Personality predictors of contamination and redemption
In the model containing TIPI, Conscientiousness was a significant predictor (β = 0.32, z = 2.10, p
< 0.05, f
2 = 0.13), where higher levels of Conscientiousness were associated with increased odds
of a redemption sequence. In the model containing, no measure of personality significantly
predicted the likelihood that a nostalgic description contained a contamination vs. redemption
sequence.
Discussion
In a sample of 744 U.S. participants aged 18-82, we assessed the content of 2,100 nostalgic
experiences as evoked by self-selected music using manual and automated coding methods. We

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showed that self-selected nostalgic songs were overwhelmingly released during participants’
adolescence and early adulthood, or during the same periods from their parents’ lives, replicating
previous work on reminiscence bumps. We observed that the content of music-evoked nostalgia
features the self most prominently, followed by social relationships with significant others,
friends, and parents. We found that positive emotion words were used more frequently than
negative emotion words, and that word valence was predicted by individual differences. Finally,
we showed that the affective arc of nostalgic narratives (contamination versus redemption) was
mostly stable across participants, but redemption arcs were predicted by individual differences.
Throughout the discussion below, we apply nostalgia’s functions as a driver self-continuity,
emotion regulation, and social connectedness as a lens through which to interpret these findings.
Reminiscence bumps across generations
Our first central finding was that self-selected nostalgic songs were released disproportionately
during adolescence and early adulthood, peaking for individuals older than thirty, between the
ages of 9 and 33. This replicates decades of research observing reminiscence bumps in this age
period for general autobiographical memory (Nápoles & Chadiha, 2011)(Conway et al., 2005;
Curci et al., 2024; Janssen et al., 2005; Rubin & Schulkind, 1997) and music-evoked
autobiographical memory (Bartlett & Snelus, 1980; Cornell Kärnekull et al., 2020; Jakubowski
et al., 2020, 2021; Krumhansl & Zupnick, 2013; Platz et al., 2015; Rathbone et al., 2017;
Renwick & Woolhouse, 2023). We replicate past work in the context of self-selected nostalgic
music that has inherent personal value to participants. This finding emphasizes that adolescence
to early adulthood is a crucial period for pairing music with memory and eventual nostalgic
experiences. The musical reminiscence bump likely exists for many of the same reasons that it
does for general autobiographical memory (for review: Munawar et al., 2018): adolescence and

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early adulthood is a time of immense novelty and identity formation, and cultural expectations
often place particular value on events from these time periods. Adolescence is additionally a
period in which individuals form their musical preferences (Holbrook & Schindler, 1989). The
presence of the reminiscence bump in nostalgic music speaks to the identity and self-focused
functions of nostalgia.
Additionally, we found a substantial portion of nostalgic songs were released before
participants’ birth year. These songs were most commonly released instead during participants’
parents’ reminiscence bumps, again between the ages of 10 and 30. This replicates previous work
on cascading reminiscence bumps for music (Jakubowski et al., 2020; Krumhansl & Zupnick,
2013). This finding speaks to the social nature of nostalgic and memory-evoked music. Crossgenerational sharing of personally relevant music may imbue later feelings of social
connectedness as an individual reminisces about shared musical moments with their caregiver.
Future work could further examine cross-generational nostalgia sharing by assessing whether
reminiscence bumps cascade in the opposite direction for older adults with children.
Music-evoked nostalgia as a self-relevant emotion
We observed that the content of nostalgic experiences was highly self-salient. Participants were
featured as either the sole actor or playing a major role, situating them as the protagonists in
their nostalgic narratives. Additionally, the greatest proportion of descriptions were about periods
in life, suggesting the object of nostalgia was largely a first-person perspective of a life chapter.
The prevalence of the self was confirmed using automated methods, in which 1st person singular
words were the top most-represented words across texts, as was the Perception subcategory,
which relies on first-person experience. Similarly, in Gibbs (2021) investigation of music-evoked
nostalgia during the COVID-19 pandemic, 65.6% of descriptions contained the self as the sole

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actor. This is in contrast to Wildschut et al. (2006)’s original characterization of nostalgic content
evoked from writing prompts; there, the self played a major role 88% of the time and was a sole
actor only 5% of the time. Together, this suggests that nostalgia as evoked by music may be
more self-focused and era-focused than nostalgia as evoked by other cues. Listening to music,
while often social, can be a solitary activity. Music as used for emotion regulation and selfreflection, as is often done in adolescence (Carlson et al., 2015; Laiho, 2004; Leipold &
Loepthien, 2015; Loureiro et al., 2024; Saarikallio, 2006), may be a particularly self-focused
activity, contributing to the greater number of highly self-salient nostalgic narratives.
Relatedly, we observed several themes in music-evoked nostalgic texts that point to
aspects of nostalgia’s function in supporting self-continuity. First, time and space were highly
represented, alluding to places and moments of the past. Additionally, we observed high
weighting of both Past Focus and Present Focus, indicating that individuals were using both past
and present tense in nostalgic narratives. While Past Focus was, as hypothesized, more
represented than Present Focus, both dictionary words appeared in the top twenty mostrepresented words across narratives. Finally, the use of insight, could indicate the navigation of
current experience in the context of the past, aligning with functions of self-continuity. Past work
on nostalgia as assessed through Facebook posts showed similar findings, with concurrent usage
of past and present tense in nostalgic texts (Davalos et al., 2015). Additionally, autobiographical
memory for musical cues have shown greater use of time and space words compared to eventspecific cues (Zator & Katz, 2017). Together, these features paint a picture of a nostalgic
experience as one that pulls an individual to a past moment in time and space, drawing from the
past to inform and support the present.

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Our findings additionally allude to nostalgia’s function as a regulatory emotion. Tone was
a top-ranking summary variable, indicating that narratives contained high frequency of words
related to positivity and negativity. Replicating previous work on the affective signature of
nostalgia (Barrett et al., 2010; Hennessy et al., in press), we observed that nostalgic experiences
contained more positive emotion than negative emotion words. Positive emotion and positive
tone words were among the top twenty most represented words across texts. This aligns
additionally with previous work on the content of nostalgia (Davalos et al., 2015; Fetterman et
al., 2024; Madoglou et al., 2017) and music-evoked autobiographical memories (Jakubowski &
Ghosh, 2021; Janata et al., 2007; Yinger & Springer, 2022). Diverging from Wildschut et al.
(2006) and our hypotheses, we observed that most narratives did not contain a redemption or a
contamination sequence (~10% in our study vs. up to 91% in Wildschut et al., 2006).
Additionally, among those that did, we found a slightly greater proportion of contamination
versus redemption sequences in nostalgic narratives, which is the opposite of Wildschut et al.
(2006)’s finding. One reason for discrepancies could be the differences in human scoring
procedures between studies. However, scorers for this study used language and examples taken
directly from Wildschut et al, (2006)’s paper and coding guide to make judgements about
redemption and contamination. Given the high inter-rater reliability among scorers in this study,
it is also unlikely that individual variability in scoring this item contributed to differences.
Alternatively, this finding may reflect the differences in response length; in Wildschut’s
investigation, an average of 1000 – 1500 words were used in narratives, while only an average of
~40 words were used here. It is possible that, in order to generate a true affective arc, longer
narratives are required. For example, in automated methods of narrative arc modeling using
LIWC-22 (Boyd et al., 2022), 250 words are required. Finally, the findings observed may reflect

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differences between music-evoked and prompt-evoked nostalgic narratives, such that musicevoked nostalgic content is more stable in regard to valence. Future music-based work utilizing
redemption and contamination scoring procedures with longer texts is needed to fully interpret
this finding.
Additionally related to the functions of identity and emotion regulation, we examined the
content of music-evoked nostalgia by categorizing celebrations and losses. Celebrations and
losses may help to define moments of growth throughout adolescence and early adulthood and
thus may serve as relevant markers of change that may contribute to the binding of a song to a
nostalgic moment. In narratives including celebrations, we found that texts were more likely to
describe smaller everyday wins (i.e., “celebrating the sun rising”) rather than moderate or major
celebrations (i.e., “my high school graduation”). Losses, however, were more evenly divided
between minor, moderate, and major, indicating that the content of music-evoked nostalgia was
just as likely to center around, for example, the failing of an exam as the death of a friend. This
suggests that positively-valenced events may have a lower threshold than negative events of
objective “importance” to be remembered as nostalgic in adulthood. While Wildschut and
colleagues (2006) did not assess the level of event, they did observe that “momentous events,”
defined as what would here be considered major celebrations or losses, comprised up to 34% of
memories (compared to ~10% observed here). This tentatively suggests that music-evoked
nostalgia may be more evocative of a broader range of events, perhaps due to music’s
omnipresent nature in daily life.
Music-evoked nostalgia as a socially relevant emotion
Beyond the self, nostalgic narratives centered around social relationships. The second largest
proportion of texts were about persons, most prominently a significant other, friend, or parent.

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This was additionally reflected in LIWC features, in which social was the fourth-most
represented category of words. This finding parallels the social functions of nostalgia, in which
experiencing nostalgia can help foster social connectedness and reduce loneliness (Abeyta et al.,
2020; Cheung et al., 2016; Juhl & Biskas, 2023; Zhou et al., 2008), as an individual draws from
positive social memories from the past. This aligns with previous work on nostalgia (Abeyta et
al., 2015; Wildschut et al., 2006; Yinger & Springer, 2022) and music-evoked autobiographical
memory (Cuddy et al., 2017; Jakubowski & Ghosh, 2021; Zator & Katz, 2017). We note that we
observed a much higher proportion of responses related to significant others, as observed in
previous work (Janata et al., 2007). We interpret this to reflect differences in sampling
characteristics, in which our sample spanned a broader age range than that of Janata and
colleagues (2007). Whereas friends are a central part of the college social experience, significant
others take on a more central role in one’s life with age.
Relationship between personality and music-evoked nostalgic content
We lastly explored the relationship between personality and nostalgic content as evoked by
music. Individuals higher in sadness used more negative emotion words. Previous work from this
sample has shown similar patterns related to trait nostalgia and sadness, where individuals those
higher in sadness report feeling more negatively (Hennessy et al., in press). Contrary to our
hypothesis, we did not observe that trait nostalgia predicted positive emotion words. Past work
has shown that individuals high in trait nostalgia may engage with nostalgia more frequently as
an emotion regulation strategy (Wildschut & Sedikides, 2023) which engenders positive feelings
and generates a reward cycle to promote future nostalgic tendencies. Consequently, individuals
high in trait nostalgia tend to feel more positive when experiencing nostalgia (Hennessy et al,. in

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press). Here, however, we did not observe this effect. This may indicate that felt positive emotion
may not translate to expressed positive emotion words when describing a nostalgic experience.
We additionally observed that personality, specifically conscientiousness, predicted the
likelihood that a narrative would contain a redemption (vs. contamination) sequence. Past work
has demonstrated individuals high in conscientiousness report higher levels of optimism across
domains (Sharpe et al., 2011). Highly conscientious individuals may be particularly persistent in
pursuit of goals related to positive outcomes, which may cyclically improve optimism for future
scenarios (Sharpe et al., 2011). The increased likelihood of conscientious participants to describe
a redemption arc may thus reflect this optimism, in which these individuals are able to reframe
more negative nostalgic memories into an “it was all for the best” mindset. Together, these
findings suggest that some dimensions of personality contribute to both the stable affective scope
through which individuals articulate memories and the narrative’s valence arc.
Limitations and Future Directions
Several limitations of this study are worth noting. First, as the primary purpose of this report was
to describe music-evoked nostalgic experiences, we did not provide a non-nostalgic control
condition for comparison. For example, past work has compared music-evoked autobiographical
memories to television-evoked (Jakubowski et al., 2021) or face-evoked (Belfi et al., 2016)
autobiographical memories. This was outside the present study's scope but warrants future
research.
Secondly, the responses given by participants were relatively short. This was intentional
(the prompt included “briefly”) to allow for the feasibility of having multiple songs and measures
while keeping the survey to a reasonable length. As such, most participants described their
nostalgic experiences in one to three sentences. Future work could examine the content of music-

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evoked nostalgic experiences with longer descriptions to allow for more in-depth processing of,
for example, narrative arcs using LIWC-22 (Boyd et al., 2022) or other methods. Despite this,
our results support existing work on general nostalgia and music-evoked autobiographical
memories, even from short texts, suggesting that nostalgic content can be conveyed in relatively
few words.
Future work could also explore the relationship between content and adaptive outcomes
with music-evoked nostalgia. Past work with nostalgia has shown that positive outcomes of
nostalgia, such as increased feelings of social connectedness and meaning in life, are
significantly moderated by the setting and content of nostalgia (Fetterman et al., 2024; Newman,
2022). These findings have yet to be explored in the context of music-evoked nostalgia.
Relatedly, in the present study, we did not ask participants to rate how vivid the music-evoked
nostalgic experiences and autobiographical memories were. It is possible that memories evoked
by nostalgic music are more or less vivid depending on the content. Future work could include
self-reports of nostalgia and memory vividness.
Constraints on Generality
While we made an effort to increase the sample size, geographic location, and age
spectrum of our study in comparison to past work, our sample was not representative of the U.S.
or global population. We did not representatively sample in terms of age, ethnic identity, or racial
identity. We additionally did not collect information about race or ethnicity, so we cannot
compare all demographic characteristics of our sample to our intended population. Future work
should recruit a representative sample to increase generalizability. Additionally, nostalgia is a
cross-cultural emotion (Hepper et al., 2014, 2024). Our sample is limited to the United States.

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Future work could examine whether the content of nostalgia and the relationship between
content and personality, affect, and positive outcomes vary across cultures.
Conclusions
In this study, we show that nostalgic experiences prompted by self-selected music are highly
self-salient, social, and positive, reflecting nostalgia’s adaptive psychological functions. Findings
suggest that music-evoked nostalgia may differ from nostalgia evoked by other cues in that it is
more self-focused and involves more everyday moments in comparison to momentous events.
Future studies should compare music-evoked nostalgia more directly with other sensory cues and
employ representative sampling across cultures.

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Chapter 3: Neural correlates of music-evoked nostalgia in younger and older adults
Abstract
Nostalgia is a mixed emotion that is often evoked by music. Nostalgic music may induce
temporary improvements in autobiographical memory in individuals with cognitive decline.
However, the neural mechanism underlying music-evoked nostalgia and its associated memory
improvements is unclear. We aimed to elucidate how nostalgia-evoking music may help retrieve
autobiographical memories in individuals with cognitive impairment by first clarifying these
processes in healthy younger and older adults. Methodological constraints including the lack of
personally-tailored and experimentally controlled stimuli have limited understanding of this
mechanism. We utilized an innovative machine-learning-based method to identify three
categories of songs: (1) personalized nostalgic, (2) familiar non-nostalgic, and (3) unfamiliar
stimuli matched for musical features. In 57 participants (29 aged 18-35; 28 aged 60 and older),
we investigated the functional neural correlates of music-evoked nostalgia using fMRI. We
observed four main findings: 1) Listening to nostalgic music, more than familiar non-nostalgic or
unfamiliar music, was associated with bilateral activity in the default mode network, salience
network, reward network, medial temporal lobe, and supplementary motor regions, 2)
Psychophysiological interaction models (PPI) indicated that listening to nostalgic music involved
functional connectivity of Heschl’s gyrus (HG) with self-referential (angular gyrus,
posteromedial) and affect-related regions (insula). In contrast, non-nostalgic music was
associated with co-activation of HG with other feature-processing regions (i.e., superior temporal
gyrus, supramarginal gyrus), 3) Older adults had greater activity compared to younger adults in
nostalgia-related regions during nostalgic listening, 4) While the neural response to nostalgic
music in younger adults was modified only by trait-level factors of nostalgia proneness and

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cognitive ability, the response in older adults was modified only by affective response tendencies
(i.e., positive feelers vs. negative feelers). This suggests that while neural correlates of nostalgic
music listening may stabilize across the lifespan regarding person-level characteristics, they
become more variable with differences in affective responding. Findings may serve as a
foundation for understanding the neural basis of music-evoked nostalgia for future clinical
interventions.

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Introduction
Nostalgia, “a wistful or excessively sentimental yearning for a return to or of some past period or
irrecoverable condition” (Merriam-Webster, 2024) is a pancultural emotion (Hanson et al., 2022;
Hepper et al., 2024; Saarikallio et al., 2020). Nostalgia’s affective profile is comprised primarily
of positive (Hepper et al., 2012; Leunissen et al., 2021; Sedikides et al., 2015; Wildschut et al.,
2006) with peripheral negative components (Hepper et al., 2012; Holak & Havlena, 1998; Turner
& Stanley, 2021) , making it an unevenly mixed emotion. Nostalgia is intertwined with
autobiographical memory (Wildschut et al., 2006), associated primarily with self- and socially
relevant memories from one’s past (Ismail et al., 2022; Madoglou et al., 2017; Wildschut et al.,
2006). It can be evoked by visual stimuli (Kikuchi & Noriuchi, 2017; Oba et al., 2015), olfactory
triggers (Matsunaga et al., 2011; Reid et al., 2015), and music (Barrett et al., 2010; Cheung et al.,
2013). When listening to music, nostalgia is a top-reported emotion (Jakubowski & Ghosh,
2019), making music an ideal stimulus from which to study this emotion. While a vast literature
has documented the psychological underpinnings of nostalgia and, to a lesser degree, musicevoked nostalgia, we lack a basic understanding of how nostalgia is processed in the brain.
Music-evoked autobiographical memory and familiarity in the brain
While few studies have investigated the neural correlates of music-evoked nostalgia,
there is a growing body of adjacent research outlining the neural basis of musical-evoked
autobiographical memories (MEAMS; Janata et al., 2007). In a recent theoretical proposal, Ren
and Brown (2023) suggest that musical memory is segmented into two broad levels: 1) musical
syntactical structure memory, comprised of musical syntax and rules related to melody, rhythm,
and structure, and 2) contextual associates memory, comprised of elements of music that
contribute to episodic memory traces, emotion, and reward. While musical syntactical structure

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memory is mainly reliant on primary auditory cortex and inferior frontal gyrus, contextual
associates memory is proposed to rely on these regions in addition to areas that support affect
and reward (i.e., striatum, amygdala), and autobiographical processing (i.e., hippocampus,
default mode network). The default mode network, including the medial prefrontal cortex
(MPFC), posteromedial cortex (PMC), angular gyrus, and medial temporal lobe (MTL), is
implicated in self-referential processing and autobiographical memory (Buckner et al., 2008;
Davey et al., 2016; Qin & Northoff, 2011), making it an ideal candidate for processing of musicevoked autobiographical memories. Indeed, these regions have been reported in numerous
investigations of the neural correlates of music-evoked autobiographical memories.
In two meta-analyses of healthy younger adults, familiar music was associated with
activity in the superior frontal gyrus (SFG) (Freitas et al., 2018; Vuong et al., 2023),
supplementary motor area (SMA)/pre-SMA (Freitas et al., 2018; Vuong et al., 2023), inferior
frontal gyrus (IFG) (Vuong et al., 2023) superior temporal gyrus (STG) (Vuong et al., 2023),
middle temporal gyrus (MTG) (Vuong et al., 2023), thalamus (Freitas et al., 2018) and precentral
gyrus (Vuong et al., 2023). Beyond familiarity, memory for popular songs appears to engage
broadly the MPFC (Ford et al., 2011; Janata, 2009; Kubit & Janata, 2018), ventrolateral
prefrontal cortex (Janata, 2009) posterior cingulate cortex (PCC) (Ford et al., 2011; Janata,
2009), and MTL (Ford et al., 2011). Specifically for music that was rated as familiar,
autobiographically salient, and pleasing, Janata (2009) revealed activity in primarily leftlateralized MPFC, ventrolateral prefrontal cortex (VLPFC), and PCC. These areas additionally
tracked the time-varying tonal structure of the music played during the scan. In a later
investigation, Kubit and Janata (2018) observed that attending to memories of music involves the
co-activation of the default mode network (DMN), hippocampus, and sensory and motor regions.

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These findings have additionally been observed across the age spectrum in studies
investigating familiar, liked, and loved music selected as a proxy for memory-evoking music. In
healthy older and younger adults, familiarity with popular songs was associated with increased
dorsomedial prefrontal cortex (DMPFC), ventral prefrontal cortex (VMPFC), lateral parietal
lobe, and MTL activity (Ford et al., 2016).
Between age groups, familiarity was associated with greater activity with medial limbic
regions for younger adults and with lateral parietal, temporal, and superior midline regions in
older adults (i.e., anterior cingulate (ACC) and PCC) (Ford et al., 2016). When listening to wellliked familiar music, healthy older adults showed activation in auditory (STG, MTG) and default
mode regions (MPFC, PCC, inferior parietal lobule (IPL)), and self-selected music additionally
activated parahippocampal regions (Quinci et al., 2022). When listening to loved music, younger
and older adults showed engagement of MPFC, PCC, precuneus, orbital frontal cortex (OFC),
paracingulate, and lateral occipital cortex (Belden et al., 2023). Age-related differences
additionally demonstrated that, while younger adults showed greater activation during loved
music in SFG, parahippocampal gyrus, brainstem, and SMA, older adults showed greater activity
in the ventral striatum, brainstem, and cerebellum (Belden et al., 2023).
In investigations of functional connectivity during liked and loved music in younger and
older adults, individuals across the age spectrum show greater connectivity between auditory and
reward regions associated with song liking (Belden et al., 2023) and temporal mesolimbic
connectivity involved in self-selected music listening (Faber et al., 2023). Between age groups,
younger adults additionally demonstrated stronger functional connectivity within networks
(auditory to auditory and reward to reward) and between networks (reward to auditory) during
listening than older adults. In contrast, older adults showed more functional connectivity between

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reward and auditory regions to out-of-network regions (i.e., sensorimotor regions, IFG,
occipital). Similarly, Faber et al. (2023) observed that older adults show patterns consistent with
age-related dedifferentiation, in which network-level distinctions between music listening
conditions (i.e., self-selected vs. experimenter-selected) were less pronounced than younger
adults. After a 12-week music listening intervention with the same healthy older adult
participants, functional connectivity increased between auditory and reward regions (MPFC)
and, for most-liked songs, within the default mode network (Quinci et al., 2022).
In sum, while no study has investigated music-evoked autobiographical memory
specifically in older adults, findings from investigations of familiarity and preference indicate
that familiar, well-liked music engages auditory cortex, the default mode network (MPFC, PCC,
IPL), and reward regions, and co-activate auditory and reward regions across the age spectrum.
Older adults additionally demonstrate greater out-of-network functional connectivity compared
to younger adults. However, differences between age groups in task-related activation show
heterogeneity across studies and require further investigation.
Nostalgia in the brain
While these studies provide critical information on the neural regions involved in
listening to preferred, familiar, or memory-evoking music, few studies have examined the neural
correlates of specifically nostalgic music that is both affect-laden and memory-evoking. Yang
and colleagues (Yang et al., 2022, 2023) proposed a model of nostalgic processing in the brain,
not specific to music, involving overlapping hubs related to autobiographical memory (MPFC,
precuneus, VMPFC, hippocampus), emotion regulation (ACC, VMPFC), self (MPFC, VMPFC,
PCC, precuneus) and reward (substantia nigra, ventral tegmental area, striatum).

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To our knowledge, only five studies have explicitly explored the neural correlates of
nostalgia (Barrett & Janata, 2016; Matsunaga et al., 2013; Oba et al., 2015; Trost et al., 2012; M.
Zhang et al., 2022). Of these, only two have investigated nostalgia as evoked by music (Barrett
& Janata, 2016; Trost et al., 2012). Studies using pictures to elicit nostalgia detected activation in
memory and reward regions, including the hippocampus, ventral tegmental area (Oba et al.,
2015), and ventral striatum (Oba et al., 2015), as well as the supramarginal gyrus (SMG), OFC,
and lateral occipital cortex (Zhang et al., 2022). In a small PET study of odor-evoked nostalgia,
Matsunaga and colleagues (2013) observed activity in the precuneus and medial OFC.
Using musical stimuli, Trost and colleagues (2012) conducted one of the first large-scale
investigations of music-evoked emotions, demonstrating that positive, low-arousal emotions
such as nostalgia and tenderness were associated with activity in the VMPFC, hippocampus,
right striatum, and OFC. However, this study used classical music that was not well known to
participants, and they observed that nostalgic music did not show activation greater or different
from related emotions (i.e., tenderness, transcendence) (Trost et al., 2012). Barrett and Janata
(2016) conducted the first and only investigation focusing on music-evoked nostalgia using
familiar music. In their study, twelve young adult participants listened to experimenter-selected
music that varied in its ability to elicit nostalgia. The authors reported no voxels associated with
nostalgia ratings across individuals. However, regions involved in emotion processing and
reward, including the insula and substantia nigra, tracked the tonal structure of nostalgia-evoking
music. Additionally, when examining the interaction of nostalgia rating and personality, they
observed that participants with lower trait nostalgia and higher trait sadness had increased
activity in reward regions during highly nostalgic songs.

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Limitations in the present literature
These studies provide a strong scientific premise for music's ability, both self- and
experimenter-selected, to engage neural networks involved in memory, self-referential
processing, reward, and sensorimotor activities. However, several questions are left unanswered.
First, while several studies investigate music-evoked emotions, musical familiarity, and musicevoked memories, there is still little research on music-evoked nostalgia specifically. Given
nostalgia’s highly affective nature, it is likely that music-evoked nostalgia recruits different or
greater neural regions than musical familiarity or music-evoked autobiographical memory alone.
For example, Quinci and colleagues (2022) observed that self-selected, familiar, and well-liked
music was most effective at driving activity in auditory and reward areas in healthy older adults,
suggesting that using nostalgic music as a stimulus may yield more robust findings. The one
study that did investigate this concept specifically (Barrett & Janata, 2016), however, found
minimal activity for their general nostalgia regressor, which may have been due to their small
sample size (N = 12) or stimulus selection method.
Relatedly, a second key unanswered question is whether self-selected nostalgic music, as
compared to experimenter-selected music, may be accompanied by different or stronger neural
activation. Many studies utilize participant-general stimuli, taking songs from the Billboard Top
100 from participants’ adolescence (Barrett & Janata, 2016; Ford et al., 2016; Janata, 2009).
However, as online platforms increase accessibility to less popular music and radios become
obsolete, nostalgia will likely be evoked by music outside a given year’s most popular songs.
Additionally, this stimulus selection method ignores the broad and heterogeneous range of
human musical experiences that may be influenced by culture, age, personality, and life
experiences, fundamentally limiting the scope of research. By 2040, nearly half of older adults in

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the United States are expected to come from diverse racial/ethnic and socioeconomic
backgrounds (Vincent, 2010). Therefore, selecting music that aligns with cultural preferences for
therapeutic purposes is an important consideration.
A more informative method of capturing the experience of music-induced nostalgia
would be through personalized stimuli, where each participant rates musical pieces for their
ability to evoke nostalgia. While some studies have utilized participant-selected music (Belden et
al., 2023; Quinci et al., 2022; Thaut et al., 2020), pieces are typically chosen based on their
enjoyment-producing characteristics, not nostalgia or memory-evoking qualities. Therefore, the
observed effects compared to experimenter-selected music may be due to a preferred set of
musical features and not to the fact that the music evoked a highly affective memory. For
example, behaviorally, Irish et al. (2006) reported reduced anxiety associated with non-memoryevoking music as compared to silence, and El Haj et al. (2012) found that memories recalled
after music listening contained more emotionally positive words, suggesting that effects may
have been due to a positive mental state induced by pleasurable music. It may be that any
pleasant or emotionally potent musical stimulus is enough to elicit vivid memories and that
nostalgia-evoking music (that is, music that is autobiographically salient and elicits a state of
mixed affect) does not produce additional neural activation.
Another limitation of current research utilizing participant-selected music is the need for
systematically chosen non-nostalgic control music to serve as comparison stimuli within each
individual. To accurately assess the effects of personalized nostalgia-evoking music,
experimenters must present participants with another selection of familiar music that shares the
same musical features, such as mode or tempo, and the nostalgia-inducing stimuli yet do not
elicit nostalgia. Several studies have worked to address this by playing musically matched

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composed music (Fischer et al., 2021; Thaut et al., 2020). These methods may capture, to a
degree, some of the variance attributable to musical features but not control for familiarity.
Finally, no study to our knowledge has examined the neural correlates of music-evoked
nostalgia in younger and older adults. Behavioral evidence points to the preservation of musicevoked autobiographical memories in older adults with Alzheimer’s Disease (AD) (Baird et al.,
2018; Cuddy et al., 2015) and the positive impact of personalized music listening for AD
psychological symptom relief (Lineweaver et al., 2021; McCreedy et al., 2022). Preliminary
neuroimaging evidence suggests that familiar and preferred music may activate many of the
same neural regions in patients with Alzheimer’s Disease as those observed in healthy younger
and older adults and highlight the particular importance of the medial prefrontal cortex in musicevoked autobiographical memories (Belfi, Karlan, et al., 2018). However, these studies are
limited by many of the same factors present in healthy adult literature. Thus, understanding the
mechanism underlying music-evoked nostalgia across the lifespan is the first step toward
understanding these mechanisms in older adults with AD. Given AD's clinical and neural
diversity, it is reasonable to first examine these processes in healthy adults.
Present study
In the present study, we will address the limitations in the literature by 1) Assessing the neural
mechanisms underlying the experience of music-evoked nostalgia, 2) Allowing participants to
self-select all of their nostalgic pieces of music, 3) Including nostalgic, non-nostalgic familiar,
and non-nostalgic unfamiliar music to examine whether nostalgia-evoking music is distinguished
from familiar music in the brain, 4) Tailoring non-nostalgic stimuli for each individual, such that
control stimuli are manipulated for familiarity and matched based on musical and acoustic
features, using a machine-learning algorithm (Hennessy et al. in press), 5) Including both healthy

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younger and older adults to examine how the neural correlates of music-evoked nostalgia may or
may not change across the lifespan. Given the impact of individual differences demonstrated in
Barrett and Janata (2016), we will also include several trait-level measures that may influence
activation associated with music-evoked nostalgia (i.e., trait nostalgia).
With these methods, we aim to assess the neural basis of how music evokes nostalgia across
the lifespan and how the neural signature of nostalgic music is different from that of musically
matched familiar and unfamiliar music. We secondarily will assess how individual differences,
such as cognitive ability, trait nostalgia, and tendency to feel positive or negative during
nostalgic listening, may impact the neural activation associated with nostalgia. We hypothesize
that 1) BOLD activity, as measured with fMRI, in the DMN, MTL, and reward networks will be
greater when listening to self-selected nostalgia-evoking music compared to familiar nonnostalgic music and unfamiliar music, 2) Functional connectivity between DMN, auditory
cortices, and reward will be greater when listening to self-selected nostalgia-evoking vs. familiar
non-nostalgic control music vs. rest, 3): Older and younger adults will demonstrate similar
patterns of neural activity. We expect that age-related differences may emerge, but the nature of
this effect (i.e., what neural regions will show differences) is exploratory, given the mixed
findings in previous work. Hypotheses related to individual differences remain exploratory.
However, we predict that cognitive ability will not influence neural activation during nostalgic
listening and that individuals higher in trait nostalgia will experience greater activation in
hypothesized regions during nostalgic listening.
Methods
All procedures were approved by the Institutional Review Board of the University of Southern
California (IRBUP: UP-22-00569). This study and its hypotheses were not pre-registered. All

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materials, including musical stimuli, datasheets, and analysis code, are available online at
https://osf.io/jw4bz/. All z-maps referenced in Results are available at Neurovault at
https://neurovault.org/collections/FEHSKFWF/.
Participants
We recruited 60 right-handed English-speaking participants from the Los Angeles area,
consisting of 30 young adults (ages 18-35) and 30 older adults (ages 60+). Inclusion criteria were
as follows: 1) fluent in English, as determined by self-report and ability to complete prescreening questionnaire without difficulty; 2) had no contra-indication to participate in an MRI
study (i.e., no irremovable metal implants, pacemakers, etc.), 3) right-handed, to reduce fMRI
data variability due to differences in handedness, 4) not currently experiencing psychiatric
symptoms by self-report, and 5) no history of neurologic disorders, 6) a score of 26 or above on
the Montreal Cognitive Assessment (MoCA; Nasreddine et al., 2005) to ensure that participants
did not have evidence of Mild Cognitive Impairment.
Participants were recruited from online research platforms, Facebook advertisements, and
the USC Undergraduate Subject Pool from November 2022 to March 2023. In total, 258
participants were screened for eligibility, with 198 failing to meet different inclusion criteria. Of
these, 19 were excluded due to MRI contraindications, 95 due to loss of interest, 3 received a
score lower than 26 on the Montreal Cognitive Assessment (MoCA), 33 were non-right-handed,
2 had neurological diseases, 1 participant was located outside Los Angeles, 7 fell outside the
specified age range, 1 had a history of stroke, 5 had experienced traumatic brain injuries (TBI),
and 19 had vascular diseases. After applying these exclusion criteria, 60 participants were
deemed eligible to participate in the study and underwent MRI scanning. Three participants were
excluded from the final analysis: 1 due to a brain abnormality detected during the anatomical

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scan and 2 due to incomplete data, resulting in a final sample size of 29 younger adults and 28
older adults. See Table 1 for demographic characteristics.
Table 1
Demographic characteristics of sample
Younger
Mean (SD)
Older
Mean (SD)
N 29 28
Gender
Female (%) 55.17 35.71
Male (%) 44.83 64.29
Nonbinary (%) 0 0
Age 22.61 (2.59) 68.46 (5.54)
Musicianship
Music training score 10.41 (7.65) 13.89 (9.04)
Plays instrument (%) 75.86 78.57
Age at training onset 8.36 (3.05) 9.41 (2.86)
Absolute pitch (%) 0 3.57
MOCA Score 28.14 (1.38) 27.68 (1.36)
Years of Education 15.59 (1.05) 16.79 (1.42)
Trait Nostalgia 5.03 (0.84) 4.52 (1.10)
Chapter 3 Table 1

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Power analysis
A sample size of 60 was chosen based on a power analysis using existing fMRI literature
(Jennings & Vance, 2002; Belfi et al., 2018; Kaufman et al., 2007; Younes et al., 2019). Regionof-interest analyses of contrasts similar to those in the current project yielded effect sizes ranging
from 0.78 to 2.4. To capture a minimum effect size of d = 0.78, at an alpha level of 0.05, with a
power of 0.80 to detect a difference in percent signal change, approximately 25 participants per
group were needed. We included an additional 5 participants per group to account for a ~20%
dropout or missing/incomplete data.
Procedure
General procedures for this study involved an online screening, a one-hour online Zoom
screening, an online stimulus selection survey, a two-hour visit comprised of an fMRI scan and
follow-up autobiographical memory task, and a 20-minute follow-up Zoom visit comprised of an
autobiographical memory task and song appraisals. After completing the study, participants were
thanked for their time and compensated $60 USD. Each portion of the procedure, including the
materials used, is described below.
Screening
Participants completed screening Informed Consent and then were screened for eligibility using
REDCap (Harris et al., 2009, 2019). REDCap (Research Electronic Data Capture) is a secure,
web-based software platform designed to support data capture for research studies, providing 1)
an intuitive interface for validated data capture; 2) audit trails for tracking data manipulation and
export procedures; 3) automated export procedures for seamless data downloads to common
statistical packages; and 4) procedures for data integration and interoperability with external
sources. In the screening survey, participants were asked to report their age, gender, history of

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neurologic, psychiatric, and vascular disorders, history of traumatic brain injury, English fluency,
handedness, and primary contact information. If initially eligible, participants were contacted to
complete an MRI safety screening form, which included questions related to MRI
contraindications (i.e., metal implants, presence of pacemaker).
If no MRI contraindications were noted, participants were invited to participate in a onehour screening visit via Zoom (Zoom.us). Informed Consent was obtained through a secure
electronic form during the Zoom meeting. Participants then completed the Montreal Cognitive
Assessment (MoCA; Nasreddine et al., 2005) online with a MoCA-certified experimenter to
assess the presence of Mild Cognitive Impairment. This task includes measures of memory,
language, attention, visuospatial skills, mental calculation, and orientation. The full version of
MoCA was used, with amendments made for several components due to the videoconferencing
platform. Specifically, during the Alternating Trail Making task, we amended the task by asking
participants to use their mouse or finger to draw on a shared screen using Zoom's “Annotate”
function. Secondly, during the Cube and Clock drawing tasks, participants were asked to
complete their drawings on a piece of physical paper and then to hold their drawings to the
camera so that the researcher could take a screenshot of the drawing. Third, during the Attention
task, in which the experimenter read a list of letters and asked the participant to clap on the letter
“A,” participants were asked to hold their hands to the camera to be visible as they clapped. Due
to the approximate one-second delay of the Zoom call, experimenters were trained to watch and
listen for the participant’s hand clap during the letter directly following the letter “A,”
corresponding to one second after the prompt. We chose to use these methods on the full version
of the MoCA rather than using the telephone version (T-MoCA) to enable the completion of all
portions of the cognitive assessment (T-MoCA excludes all drawing sections). These methods

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were piloted in a small group of older adults for feasibility and were found to be easy to
complete for this age cohort. If a participant scored below 26/30, they were notified that they
were ineligible for the remainder of the study. They were encouraged to seek additional testing
and referred to resources at USC (adrc.usc.edu) and the Alzheimer’s Association (alz.org).
Participants who were ineligible at this stage received $20 for their participation in the Zoom
visit. If participants scored 26 or higher on the MoCA, they were notified that they were eligible
for the remainder of the study. They were then asked to complete an online stimulus selection
survey via Qualtrics (Qualtrics, 2022).
Online music personalization survey
Eligible participants were asked to complete an online stimulus selection survey via Qualtrics
(Qualtrics, 2022). This survey aimed to identify six songs selected by the participant that were
known to evoke nostalgia, and to identify non-nostalgic “Familiar Control” and “Unfamiliar
Control” songs. Familiar Control songs were intended to be musically-matched, familiar, nonnostalgic, and Unfamiliar Control songs were intended to be musically-matched, unfamiliar
songs. This was accomplished by using a control song selection tool (described below)
embedded via Javascript into the backend of the Qualtrics survey to allow interactivity.
This survey took approximately 40 minutes to complete. Participants were instructed to
complete the survey in a quiet space with headphones or speakers. Audio quality was tested at
the beginning of the survey. During the survey, they were asked to do the following:
1. List six nostalgia-inducing songs.
2. Listen to each of the self-reported Nostalgia songs and up to ten candidate Control songs
as identified by the control song selection model (see below).

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3. Appraise songs based on familiarity, valence, and arousal for each song and a freeresponse prompt regarding why such songs are nostalgic.
4. Complete psychological and personality measures.
5. Report demographic information (i.e., music training).
Participants were given definitions of nostalgia (“sentimental longing for the past”) and of a
nostalgia-evoking song (“a song that brings you back to a pleasant moment or era of your life
and evokes a strong memory”). Then, they were asked to complete a comprehension check, in
which they were asked to choose the definition of “nostalgia” and of a “nostalgia-evoking song,”
“as this study defines it,” from a list of five options. The survey would not let participants
continue until they had chosen the correct response. The options for both prompts are listed in
the Supplemental Methods (Appendix C). Then, participants were asked to enter six nostalgiainducing songs into a form that fed into our control song selection model (see below). Setting
these six songs as seeds, we then used Spotify's API to find recommendations for songs released
within five years of each seed song, with valence and energy ratings within .15 points (out of 1)
of the seed song and popularity of at least 80. If Spotify could not generate recommendations for
an input song, a respondent was prompted to enter another nostalgic song.
After inputting three nostalgia-inducing songs, participants were then presented a 30-
second clip from a maximum of 66 songs in random order: the six songs that they input as
nostalgic and up to ten songs that were recommendations for each nostalgic song presented in a
set, which we will call “candidate Control songs.” Specifications for the model used to generate
these candidates are reported below (Control Song Selection Model). The 30-second clip was
chosen by Spotify’s automatic preview by a privileged music segmentation algorithm. After each

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song, participants rated their familiarity with the song (“Not at all familiar,” “Somewhat
familiar,” “Very familiar”). If songs were rated as “Somewhat familiar” or “Very familiar,”
participants rated how nostalgic the song made them feel on a scale from 1 (“Not nostalgic at
all”) to 9 (“Extremely nostalgic”). If a subject labeled a candidate Control song as sufficiently
familiar (“Somewhat” or “Very”) and sufficiently not nostalgic (< 5 on the nostalgia rating
scale), the song would be selected for the experimental paradigm, and the other candidate
Control songs from the set would not be presented to that subject. If a song was either too
nostalgic or unfamiliar, participants would continue to listen to the next Control song candidate
(see Figure 1). Unfamiliar Control songs were selected for each seed song from the list of songs
participants had indicated as “Not at all familiar.” If a suitable Familiar Control or Unfamiliar
Control song was not found for one or more of the Nostalgic songs after ten tries, participants
were presented with additional Control Song candidates at the beginning of their in-person visit,
before the fMRI scan until all Familiar Control and Unfamiliar Control songs were identified.

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Figure 1.
Control song selection model.
Note. Δ = difference between seed song (Nostalgic song) and candidate Control song.
This was the case for most participants, in which more than ten candidate Control songs
were required for 1-2 songs in 25 participants, 3-4 songs in 20 participants, and 5-6 songs in 1
participant. For 11 participants, 1-2 Nostalgic songs could not be matched to appropriate
Familiar Control or Unfamiliar Control songs, even after presenting additional candidates, due to
unfamiliarity with or feeling too nostalgic in response to candidates. In these cases, the
unmatched stimulus set was dropped from the analysis for that participant. By the end of the
Chapter 3 Figure 1

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control song selection process, participants with complete sets had six Nostalgia songs, six nonnostalgic, Familiar Control songs, and six Unfamiliar Control songs.
Control song selection model. To identify control songs that were musically matched to
participant-selected Nostalgia songs, we developed a music-matching tool using Spotify’s web
API (Lamere, n.d.). Spotify’s API is a free tool for Music Information Retrieval (MIR) that
indexes any song from Spotify’s library for various musical features, including key, tempo,
loudness, danceability, valence, energy, popularity, mode, acousticness, liveness, and
instrumentalness. The control song selection tool took in a user-inputted nostalgia-evoking song
and recommended a set of “candidate Control songs,” all of which were matched based on the
similarity of three key Spotify API features: valence, energy, and release date. Valence, defined
as a musical piece’s positiveness, was measured on a scale from 0.0 to 1.0, where 1 is highly
positive. Energy, defined as a song’s intensity or activity, was measured from 0.0 to 1.0 and
aligns with the psychological concept of arousal. The release date was additionally manipulated
to ensure that Nostalgia and Control song pairs would evoke familiarity from the same period of
a participant’s life and to Control for era-dependent musical style variations. We set a minimum
threshold for popularity, a Spotify metric ranging from 0 to 100, to enhance the likelihood that a
song would be rated as “familiar” to a given participant. Our previous work indicated that
manipulating these key features was sufficient to provide songs that were matched for all other
available Spotify API features (Greer, et al., under review; Hennessy et al., in press)
We used Spotify's Recommendations call to compile a list of four candidate Control
songs for every Nostalgia song, in which the minimum popularity was 0.80, the valence and
energy were matched to the Nostalgia song within 0.15 points, and the release date within five
years of that of the Nostalgia song. The script for this music matching tool is available on

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https://osf.io/jw4bz/ and a personalizable web-based version, SoundsLikeThis
(soundslikethis.com), is available freely for public research and entertainment use.
Subjective appraisals of valence and arousal. While objective, expressed measures of
valence and arousal, as determined by Spotify, were controlled for each song pair, subjective
experience during listening was expected to vary. Thus, we additionally took a measure of
participants’ feelings during listening. After each nostalgia and control song, participants rated
the musical clip for felt valence using a two-part Likert question (“Rate how positive the emotion
was that you FELT while listening to the song” and “Rate how negative the emotion was that you
FELT while listening to the song”) and arousal using a two-part Likert question (“How activated
was the emotion that you FELT while listening to the song. An example of a highly activated
emotion is excitement or fear” and “How activated was the emotion that you FELT while
listening to the song. An example of a highly deactivated emotion is sadness or calm”).
“Activation” was used to describe arousal to capture embodied feeling states; this language has
been successfully used in other investigations involving self-reporting of emotional arousal (e.g.,
Presti et al., 2022). Both sets of questions were on a scale of 0 to 10. To reduce exposure to
Unfamiliar Control songs before the scan, the same set of valence and arousal questions were
probed for each Unfamiliar Control song after the conclusion of the scan during their follow-up
Zoom visit.
Trait-level measures. At the end of the online stimulus selection survey, participants
were asked to complete the 7-item version of the Southhampton Nostalgia Scale (SNS;
(Sedikides et al., 2015a), to assess trait-level nostalgia (Barrett et al., 2010; Routledge et al.,
2008). In this task, participants rated, on a 7-point scale ranging from 1 (“Not at all”) to 7 (“Very
much”), their experience of nostalgia in daily life. Items include questions related to nostalgia’s

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importance (“How significant is it for you to feel nostalgia?”) and proneness (“How often do you
feel nostalgia?”). Scores across all items are averaged (with one backward-scored item) to create
one Trait Nostalgia score for each participant.
Participants additionally completed the Music Training Questions from the Goldsmith
Music Sophistication Index (Müllensiefen et al., 2014). These scores were summed to create a
“music training” aggregate score, and individual elements were kept in their raw form for
descriptive purposes (i.e., “Do you play an instrument?”), and are reported in Table 1.
fMRI task
After completing the music personalization survey, participants were invited for an inperson visit to the University of Southern California’s Dana and Dornsife Neuroimaging Center
to complete their fMRI scan. The fMRI task was a music-listening task presented using
MATLAB version 9.13.0 (The MathWorks Inc., 2022) to play music from each participant’s
personalized stimulus folder. A block design was used (see Figure 2), consisting of Nostalgia,
Familiar Control, and Unfamiliar Control blocks, with order counterbalanced across participants.
Participants completed two functional runs containing nine blocks each, with one song played
per block. The start of each run contained a 5-second buffer. Each run contained three Nostalgia,
three Familiar Control, and three Unfamiliar Control songs, with triplets present within each run
(i.e., Nostalgia Song 1, Familiar Control Song 1, Unfamiliar Control Song 1), for a total of 18
songs across runs. Participants listened to the first 40 seconds of a nostalgia-evoking, Familiar
Control or Unfamiliar Control song during each block, as determined by the stimulus selection
procedure. Between each block, there was a 15-second rest period. Participants were told to keep
their eyes closed and remain still in the scanner for the entirety of each run. Music was played
through fMRI-safe active noise-canceling headphones that actively attenuated the noise of the

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functional scan sequence (OptoAcoustics OptoActive) to allow participants to hear the music
over the sound of the scanner.

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Figure 2.
fMRI task design (A), and example shuffling of song triplets within one run (B).
Chapter 3 Figure 2
Note. In panel B, FC = Familiar Control song block, UC = Unfamiliar Control song block, N =
Nostalgia song block, R = rest period. Numbers indicate placement within triplet (i.e., FC1 is the
Familiar Control song that is musically-matched to Nostalgia song 1 (N1) and Unfamiliar
Control song 1 (UC1)).

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Autobiographical memory task
After the fMRI scan, participants were asked to complete an autobiographical memory task
modeled after Belfi (2016) and Levine (2002). A randomly selected pair of one Nostalgia song
and its matched Familiar Control song was used as a free-recall autobiographical memory task.
Participants listened to the first 40 seconds of either the Nostalgia or Familiar Control song, with
order counterbalanced across participants. After the song, participants were asked to “verbally
describe, in detail, a memory of your past.” After an initial response, participants were prompted
to think of more details to add to their description (“Do you have any additional details you’d
like to provide?”). Participants were given a maximum of five minutes for the initial response
and two minutes for the follow-up response. After the follow-up, participants were asked if the
memory was related to the song that had been played. The audio responses were recorded. One
week after the scan, participants were invited to a follow-up Zoom visit, during which they
repeated the task for the other song in the pair. Results involving this task will be reported in a
future manuscript.
Neuroimaging parameters
A 3T Siemens MAGNETON Prisma System with a 32-channel head coil, located at the Dana
and David Neuroimaging Institute at the University of Southern California, was used for this
study. We obtained high-resolution T1-weighted structural MRI images (1mm x 1mm x 1mm
resolution, over a 256 mm x 256 mm x 256 mm FOV, TR = 2300 ms, TE = 2.05 ms; flip angle =
8°) using a 3D magnetization-prepared rapid acquisition gradient (MPRAGE) sequence.
Diffusion-weighted images were also obtained during the scan session and will be reported in a
future manuscript.

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Functional images were obtained using a gradient-echo, echo-planar T2*-weighted
multiband pulse sequence with a multiband factor of M = 8 (TR = 1000ms, TE = 35 ms, flip
angle = 52°, 68 x 68 mm in-plane resolution). 48 slices were obtained with 3 mm x 3 mm x 3
mm voxel resolution. For the nostalgic music listening task, 512 functional volumes were
obtained. The gradient-echo field map was obtained to correct for field-inhomogeneity in
analysis (TR = 1000ms, TE1 = 5.19 ms, TE2 = 7.65 ms, flip angle = 60°, 68 x 68 mm in-plane
resolution).
Data analysis
Music Stimuli Analysis
To assess whether our stimulus selection was successful in identifying musically matched nonnostalgic control songs for each participant’s nostalgic songs, we compared music stimuli across
conditions along the following Spotify-derived features: acousticness, key, mode, danceability,
energy, instrumentalness, liveness, loudness, popularity, release year, speechiness, tempo, and
valence. Due to the clustered structure of the data, in which each participant had six songs for
each condition, multi-level models were used using R version 4.3.2 (R Core Team, 2023) with R
Studio and the lme4 (Bates et al., 2015) package. A separate model was fitted for each
continuous feature (all, excluding key and mode), with Condition predicting the feature,
including random intercept for participant. If an effect of Condition was observed, effects were
further probed using Tukey’s Honestly Significant Difference with emmeans (Lenth, 2023).
Effect sizes for fixed effects were calculated as Cohen’s f
2 (Cohen, 1992) with the MuMin
package (Bartoń, 2023). Bonferroni-comparison was used for Spotify feature analyses (number
of comparisons = 11). Musical key and mode were assessed with a chi-squared test of
association, to determine whether musical key or mode categories differed by condition. Lastly,

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to estimate approximately when during a participant’s life they may have listened to their selfreported nostalgic songs, song-specific age (SSA) was calculated by subtracting a participant’s
birth year from a song’s release year. These results were plotted as descriptive statistics only, as
the release date was already included in the above models.
Behavioral Analysis
To assess differences in the subjective feeling while listening, ratings of nostalgia were assessed
between Nostalgia and Familiar Control conditions and age groups using multi-level models,
with a random slope of condition on the participant. Participant-rated felt positive valence,
negative valence, high arousal, and low arousal were assessed in separate models, again with
condition (Nostalgia, Familiar Control, Unfamiliar Control) and age group as fixed effects and a
random slope of condition on participant. Significant effects were further assessed with Tukey’s
HSD, and Cohen’s f
2 was calculated. Bonferroni-comparison was used (number of comparisons =
5).
To assess differences between age groups in trait-level measures, scores from the MoCA
and SNS were compared between age groups using a one-way analysis of variance using the lm
and anova functions from base package of R (R Core Team, 2021). Cohen’s f
2 was calculated.
Across analyses, ggplot (Wickham, 2016) was used for plotting and visualizations.
fMRI preprocessing
Pre-processing and analyses of functional images were performed using FSL version 6.0
(Jenkinson et al., 2012). Skull stripping was performed using FSL’s BET brain extraction tool.
The gradient-echo fieldmap was used to correct inhomogeneity in the magnetic field using FSL’s
FUGUE unwarping tool (anterior-posterior, 10% signal loss threshold). Motion correction was
performed using FSL’s MCFLIRT, and additional scrubbing was conducted for each functional

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run using root-mean-squares intensity differences (dvars) to identify slices that should be
regressed out during analyses (Power et al., 2012). Specifically, slices with dvars values outside
of the 75th percentile and 1.5*interquartile range were regressed out of the GLM analyses in a
confound matrix. Slice timing correction was performed with Fourier-space time series phase
shifting and spatial smoothing was performed (5.0 mm FWHM Gaussian kernel). High-pass
temporal filtering with a Gaussian weighted least-squares line (sigma = 100 s) was performed.
Functional images were initially registered to their T1 image using FSL’s FLIRT, and then further
nonlinearly registered to standard space (MNI 152 space) with a 12 degree of freedom affine
transformation using FSL’s FNIRT. Motion artifacts were then further removed using ICAAROMA (Pruim et al., 2015).
Whole Brain
To assess what regions were active during nostalgic listening versus Familiar Control and
Unfamiliar Control listening, we performed a whole-brain general linear model (GLM) analysis.
After pre-processing, a fixed effects analysis was used to combine the two functional runs of the
music task for each participant. The music task was modeled with a regressor for each music
condition (Nostalgia, Familiar Control, Unfamiliar Control), using a boxcar convolved with a
double-gamma hemodynamic response function. BOLD signal between conditions were
contrasted using a general linear model (GLM). Participant-level models were combined into a
mixed-effects analysis using FSL’s FLAME 1 to assess contrasts of each condition. Independent
sample t-tests were used to determine differences in brain activation between age groups in each
contrast, as well as activation differences across groups between conditions (Nostalgia >
Familiar Control, Nostalgia > Unfamiliar Control). We thresholded Z statistical images using
FSL’s cluster thresholding (which aims to control Family-wise error rate), with a cutoff of Z >

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3.1, and a corrected cluster-size probability of p = 0.05. For contrasts that included double
subtractions (i.e., Nostalgia > Familiar Control, Older > Younger), the directionality of the effect
was investigated using FSL’s Featquery by extracting beta values for each main effect at the
group level. Whole brain results were visualized with MRIcroGL
(https://www.mccauslandcenter.sc.edu/mricrogl/) and on a 3D surface using FreeSurfer’s
FreeView.
Region of interest
To further test our hypotheses regarding regions that would be more active during nostalgic
listening vs non-nostalgic listening, we conducted a region-or-interest (ROI) analysis. We
obtained percent signal change values in regions selected a priori due to their known
involvement in reward, autobiographical memory, and self-referential processing (medial
prefrontal cortex, posteromedial cortex, medial temporal lobe, and ventral tegmental area). We
create 8-voxel spheres with center coordinates located at peak clusters observed in published
meta-analyses on autobiographical memory, reward, and auditory processing (see Table 2). Due
to overlapping spheres on cortical midline structures (VTA, MPFC, PMC), adjusted center
coordinates were used such that the absolute value of the x coordinate had a minimum value of 8.
Percent signal change was calculated from beta values using FSL’s Featquery at each subject’s
second-level contrasts (one contrast across two runs for each participant). A repeated measures
ANOVA was conducted for each ROI, with condition (Nostalgia, Familiar Control, or Unfamiliar
Control) and side (Left or Right) as within-subjects factors and age group (younger or older) as
the between-subjects factor, with an alpha level of 0.05. For significant main and interaction
effects, pairwise comparisons were computed using Tukey’s HSD. Effect sizes were computed
using Cohen’s f
2
.

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Table 2
Center coordinates for spherical regions of interest for ROI and PPI analyses.
Chapter 3 Table 2
ROI Center coordinates Derived from Analysis
MPFC L: (-8, 54, 14)
R: (8, 54, 14)
Kim, 2012 ROI
PMC L: (-8, -59, 29)
R: (8, -59, 29)
Kim, 2012 ROI, PPI
MTL L: (-24, -33, -24)
R: (21, -29, -19)
Kim, 2012 ROI
VTA L: (-8, -16, -14)
R: (8, -14, -4)
Diekhof et al., 2012 ROI
HG L: (-32, -26, 8)
R: (54, -10, 6)
Chan & Han, 2022 PPI
Note. ROI = region of interest analyses. PPI = psychophysiological interaction analyses
(functional connectivity).
Individual Differences
To investigate our exploratory hypotheses related to the role of individual differences in the
neural response to Nostalgia and Familiar Control music, we added regressors of interest at the
group level in three separate models: 1) Cognitive Ability (as measured by MoCA; Nasreddine et
al., 2005), 2) Trait Nostalgia (as measured by SNS; Sedikides et al., 2015), 3) Nostalgia Valence

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(felt positive and negative valence averaged within the Nostalgia condition). Each set of
regressors was applied to the Nostalgia > Familiar Control, Nostalgia > Rest contrasts. The
Cognitive Ability and Trait Nostalgia models were also assessed in the Familiar Control > Rest
contrast. For all models, behavioral ratings were mean-centered across participants. Regions of
activation in each contrast predicted by the regressor of interest were assessed across and
between age groups. For contrasts that included double subtractions (i.e., Nostalgia > Familiar
Control, Older > Younger), the directionality of the effect was investigated using FSL’s
Featquery by extracting beta values for each participant at the second level (averaged across
runs) and regressed behavioral ratings. Whole brain results were visualized with MRIcroGL and
on a 3D surface using FreeSurfer’s FreeView. For the Cognitive Ability model, mean-centered
age was included as a regressor of non-interest. For Valence, positive valence was assessed while
controlling for negative valence (as a regressor of non-interest) and visa versa. Averaged positive
or negative valence across all nostalgic songs comprised the valence score for each participant
for the Nostalgia > Rest contrast and the Nostalgia > Familiar Control contrast. These measures
were intended to capture the typical response pattern of an individual listening to a nostalgic
piece of music and do not reflect song-level changes in felt or expressed valence. Due to the
focus on typical nostalgic listening, the Familiar Control > Rest contrast was excluded from the
valence analysis.
Functional Connectivity
To test our hypotheses regarding networks of interacting regions that would support the
experience of music-evoked nostalgia, we conducted a functional connectivity analysis. We
constructed psychophysiological interaction (PPI) models to test for differences in whole brain
and seed connectivity between each music condition and rest (Nostalgia > Rest, Familiar Control

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> Rest) and the Nostalgia > Familiar Control contrast across and between age groups. Seeds
were chosen for their known involvement in auditory (Heschl’s Gyrus; HG) and self-related
(PMC) processing. Seeds were 8-mm voxel spheres with center coordinates at peak clusters
observed in meta-analyses (see Table 2). At the subject level, after pre-processing (described
above), we extracted the time series of each run’s CSF, white matter using FSL’s FAST (Zhang et
al., 2001), and the time series of the masked region of interest. De-noised data was then input
into a lower-level PPI general linear model. In this model, we included 1) one regressor
reflecting task condition (psychological variable), 2) one regressor representing the timeseries for
the region of interest (physiological variable), and 3) one regressor represented the interaction
between the condition and region-of-interest (psychophysiological interaction). Timeseries of
CSF and white matter were included as regressors of non-interest. For each run and then runs
were combined for each participant in a higher-level analysis. Subject-specific motion
parameters were included as nuisance regressors. Then, at the group level, we combined data
across participants, using a cluster threshold of Z > 3.1, with a corrected cluster significance
threshold of p = 0.05 (Worsley, 2001). Results were visualized with MRIcroGL.
Results
Music Stimuli Features
The complete list of musical stimuli used in this study and their Spotify URLs are uploaded on
OSF at https://osf.io/jw4bz/. Word clouds of musical artists of participants’ self-selected
nostalgic songs are displayed in Figure 3. Nostalgia song song-specific age was younger for
younger adults (M = 7.77 years, SD = 12.23 years) than for older adults (M = 25.86, SD = 18.20)
(see Figure 4). SSA did not differ across music conditions (ps > 0.05).

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Figure 3.
Word clouds for nostalgic song artists in younger and older adults.
Chapter 3 Figure 3
Note. Size represents frequency, with artists in larger fonts appearing more frequently across selfselected nostalgic song lists. Younger N = 29, Older N = 28.
Means and standard deviations of computer-derived Spotify-MIR features are reported in
Table 3. Spotify MIR features of valence, energy, release year, danceability, loudness,
speechiness, tempo, instrumentalness, acousticness, and liveness did not differ between
conditions (Figure 4). There was a main effect of song condition on popularity (Nostalgia β =
18.00, t(913.78) = 11.48, padjusted < 0.001, f
2 = 0.12), where Nostalgia songs had greater than in
Familiar Control and Unfamiliar Control songs, and Familiar Control songs had greater

163
popularity than Unfamiliar Control songs (Figure 4). Multilevel model results for Spotify MIR
features are reported in Table S2 (Appendix C).
Table 3
Means and standard deviations (SD) of computer-derived musical features for Nostalgia,
Familiar Control, and Unfamiliar Control Songs.
Chapter 3 Table 3
Nostalgia Familiar
Control
Unfamiliar
Control
Mean SD Mean SD Mean SD
Acousticness 0.39 0.33 0.35 0.32 0.37 0.33
Danceability 0.55 0.17 0.56 0.17 0.56 0.16
Energy 0.55 0.25 0.56 0.24 0.55 0.25
Instrumentalness 0.10 0.25 0.07 0.22 0.09 0.23
Liveness 0.18 0.16 0.18 0.14 0.18 0.15
Loudness (dB) -9.67 5.61 -9.43 5.20 -9.98 5.42
Popularity 58.22 23.26 48.38 20.75 40.22 20.56
Release Year 1995 21 1995 21 1995 20
Speechiness 0.06 0.05 0.06 0.07 0.06 0.06
Tempo (BPM) 119.76 29.80 118.69 28.47 119.14 29.42
Valence 0.50 0.25 0.54 0.25 0.52 0.26
Note. Acousticness, danceability, instrumentalness, liveness, and speechiness are on a scale of 0
to 1, in which a score closer to one denotes more intensity of that feature. Valence and energy are

164
also on a scale of 0 to 1; however, 0.5 indicates the “neutral” midpoint, 1 is high or positive, and
0 is low or negative. Popularity is on a scale of 0 to 100, where higher values indicate more
streams on Spotify.

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Figure 4.
Spotify features between conditions.
Chapter 3 Figure 4

166
Behavioral Results
Subjective Song Ratings
Means and standard deviations of ratings of felt nostalgia, valence, and arousal are presented in
Table 4 and Figure 5. The intra-class correlation coefficient (ICC) for nostalgia rating was ~0,
indicating that nearly none of the variance for nostalgia ratings was at the individual participant
level before including additional variables into the model. Nostalgic songs were rated as
significantly more nostalgic than Familiar Control songs (β = 5.75, t(553.78) = 36.514, padjusted <
0.001, f
2 = 7.71) (see Table S1 in Appendix C for full multi-level model results). No differences
between age groups were observed.
Table 4
Means and standard deviations for felt nostalgia, valence, and arousal during music listening.
Chapter 3 Table 4
Nostalgia Songs
Familiar Control
Songs
Unfamiliar Control
Songs
Younger
Mean
(SD)
Older
Mean
(SD)
Younger
Mean
(SD)
Older
Mean
(SD)
Younger
Mean
(SD)
Older
Mean
(SD)
Nostalgia 8.41
(0.94)
8.59
(0.85)
2.65
(1.16)
2.56
(1.22)
Positive
Valence
8.15
(2.14)
9.07
(1.49)
3.66
(2.51)
2.87
(0.23)
3.56
(2.56)
4.82
(2.83)

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Negative
Valence
1.52
(2.17)
1.10
(2.13)
0.88
(1.50)
1.25
(2.11)
1.59
(2.12)
1.55
(2.40)
High
Arousal
6.00
(3.11)
7.47
(2.69)
2.73
(2.55)
2.99
(2.77)
2.87
(2.59)
4.23
(2.90)
Low
Arousal
3.06
(3.04)
2.67
(3.26)
1.71
(2.11)
2.07
(2.50)
2.35
(2.46)
2.18
(2.71)
Note. Higher values denote greater strength of feeling (i.e., higher positive valence indicates
greater felt positivity, higher negative valence indicates greater felt negativity). Nostalgia is on a
scale of 1 to 9 and both valence and arousal measures are on a scale of 0 to 10.
For felt valence and arousal, ICCs indicated that 13%, 20%, 20%, and 17% of the variance for
positivity, negativity, high arousal, and low arousal were at the individual participant level before
including additional variables into the model. Nostalgia songs were rated as significantly more
positively valenced than Unfamiliar Control songs (β = 4.61, t(52.46) = 13.87, padjusted < 0.001, f
2
= 0.79) and Familiar Control songs (padjusted < 0.001). Older adults had higher positivity ratings
across conditions than younger adults (β = 1.32, t(55.27) = 2.73, padjusted < 0.05, f
2 = -0.79).
Familiar Control songs were rated as significantly less negative than Unfamiliar Control songs (β
= -0.68, t(52.62) =-3.00, padjusted < 0.05, f
2 = 0.02). Nostalgia songs were rated as higher in
arousal than Unfamiliar Control songs (β = 3.14, t(55.10) = 8.75, padjusted < 0.001, f
2 = 0.41), and
Familiar Control songs (padjusted < 0.001). An interaction between age group and condition was
additionally observed (β = -1.16, t(51.46) = -2.82, padjusted < 0.05, f
2 = -0.81), such that
Unfamiliar Control songs were rated as higher arousal than Familiar Control songs in the older

168
adult group only. No additional condition, age group, or interaction effects were observed for felt
valence or arousal (See Table S1 in Appendix C).
Figure 5.
Felt nostalgia, valence, and arousal during Nostalgia, Familiar Control, and Unfamiliar Control
songs.
Chapter 3 Figure 5
Note. Error bars represent standard errors. Stars indicate statistical significance (* p < 0.05, ** p
< 0.001, *** p < 0.001). Younger N = 29, Older N = 28.

169
Cognitive Ability
Means and standard deviations of trait-level measures (MoCA score, SNS) between age groups
are presented in Table 1 and Figure 6. Cognitive ability, as measured with MoCA, did not differ
between older and younger adults (p > 0.05).
Trait Nostalgia
Trait Nostalgia, as measured with SNS, did not significantly differ between age groups (p =
0.051), but older adults trended towards lower scores than younger adults.
Figure 6.
Cognitive Ability (MoCA) and Trait Nostalgia (SNS) between age groups.
Chapter 3 Figure 6
Note. Error bars represent standard errors. MoCA = Montreal Cognitive Assessment. SNS =
Southampton Nostalgia Scale. Younger N = 29, Older N = 28.

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Whole Brain Results
All Z-maps are available on Neurovault at https://neurovault.org/collections/FEHSKFWF/.
Whole brain results for the Nostalgia > Familiar Control contrast are presented in Figure 7.
Coordinates of peak clusters are presented in Table 4. Significant activation was observed in the
Default Mode Network (bilateral PCC, bilateral precuneus, bilateral angular gyrus, bilateral
ACC, bilateral dorsomedial and ventromedial prefrontal cortices), motor regions (bilateral
supplementary motor area, bilateral precentral gyrus), occipital regions (bilateral superior lateral
occipital cortex, left occipital pole, left intracalcarine), frontal regions (left DLPFC, left VLPFC,
bilateral IFG (pars opercularis), bilateral OFC, R SFG), left MTG (temporooccipital region),
bilateral SMG, and bilateral anterior insula. Activity was also observed subcortically in the
bilateral ventral tegmental area (VTA), superior colliculus, bilateral caudate, bilateral thalamus,
bilateral posterior parahippocampal gyrus. Lastly, cerebellar activity was observed in bilateral
crus I, lobules VI, I-IV, and vermis VIIIa.
Table 4.
Coordinates of peak clusters by region for the Nostalgia > Familiar Control contrast, across
participants.
Area Z-value x y z
Frontal L ACC 5.52 -6 30 16
L anterior insula 5.96 -37 12 6
L DLPFC 4.66 -32 47 26
L DMPFC 4.79 -5 25 42
L IFG (pars opercularis) 6.09 -48 8 4
L OFC 4.03 -41 25 -12
L precentral gyrus 4.63 -46 -9 46
L SMA 5.49 -4 -1 62

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L VLPFC 4.86 -24 46 15
L VMPFC 5.66 -5 57 11
R ACC 5.24 4 32 14
R anterior insula 5.97 36 11 6
R DMPFC 4.79 1 17 50
R OFC 3.66 40 24 -14
R precentral gyrus 4.04 47 -8 41
R SFG 6.04 6 12 62
R SMA 5.76 5 6 58
R VMPFC 4.68 1 53 2
Temporal L MTG (temporooccipital
regions)
4.6 -61 -54 1
L posterior PHG 3.85 -22 -26 -26
R posterior PHG 4.36 14 -27 -14
Parietal L angular gyrus 4.03 -44 -51 36
L PCC 5.21 -6 -59 23
L precuneus 5 -10 -60 38
L SMG 4.86 -58 -42 34
R angular gyrus 3.82 46 -48 21
R PCC 4.24 3 -50 14
R precuneus 4.1 3 -61 27
R SMG 3.56 49 -38 34
Occipital L intracalcarine 4.15 -2 -87 4
L occipital pole 4.32 0 -91 3
L superior lateral occipital cortex 4.78 -43 -67 34
R superior lateral occipital cortex 4.32 52 -72 32
Subcortical L caudate 4.68 -18 15 11
L thalamus 5.29 -6 0 6
L VTA 4.27 -4 -30 -17
R caudate 4.47 18 23 6
R thalamus 5.35 4 -2 8
R VTA 3.53 3 -30 -12
Superior Colliculus 3.39 0 -20 -2
Cerebellar L crus I 5.02 -38 -59 -32
L I-IV 4.23 -6 -50 -15
L VI 5.05 -25 -60 -23

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R crus I 4.3 39 -64 -29
R I-IV 4.32 4 -53 -5
R VI 5.35 35 -50 -28
Vermis VIIIa 4.3 0 -61 -34
Note. Coordinates are in MNI space. ACC = anterior cingulate cortex, DLPFC = dorsolateral
prefrontal cortex, DMPFC = dorsomedial prefrontal cortex, IFG = inferior frontal gyrus, OFC =
orbitalfrontal cortex, SMA = supplementary motor area, VLPFC = ventrolateral prefrontal
cortex, SFG = superior frontal gyrus, MTG = middle temporal gyrus, PCC = posterior cingulate
cortex, VTA = ventral tegmental area.
Figure 7.
Whole brain results for Nostalgia > Familiar Control contrast, across all participants.
Chapter 3 Figure 7
Note. Color bar represents Z statistic. Total N = 57.

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Between-group contrasts within the Nostalgia > Familiar Control contrast revealed significant
activation in the Older > Younger age contrast (see Figure 8, top panel). For coordinates of peak
clusters, see Table 5. Specifically, activation for this contrast was observed in the bilateral STG,
right parietal operculum, bilateral planum polare, right temporal pole, bilateral MTG, right
postcentral gyrus, and left angular gyrus. Further investigation of this effect was conducted using
FSL’s Featquery to extract percent signal change from rest in each condition for each age group.
This indicated that, in the Familiar Control condition, younger adults had greater activation in
these regions than older adults, but in the Nostalgia condition, older adults had greater activation
than younger adults (See Figure 8, bottom panel).
Table 5.
Coordinates of peak clusters by region for the Nostalgia > Familiar Control contrast, Older >
Younger.
Chapter 3 Table 5
Area Z-value x y z
Frontal L STG 4.36 -58 -35 5
R STG 3.88 58 -22 2
Parietal L angular 3.96 -40 -50 23
R parietal
operculum
4.02 44 -25 16
R postcentral
gyrus
3.55 56 -9 29
Temporal L MTG 4.2 -64 -8 -14
L planum polare 3.99 -59 0 0
R MTG 4.2 63 -4 -10
R planum polare 4.29 58 0 2
R temporal pole 4.06 33 5 -37

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Note. Coordinates are in MNI space. STG = superior temporal gyrus, MTG = middle temporal
gyrus.

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Figure 8.
Whole brain results for Nostalgia > Familiar Control contrast, Older > Younger adults (top) and
bar plots of younger and older adults percent signal change from rest in the Nostalgia and
Familiar Control condition, within clusters significant in Nostalgia >Familiar Control, Older >
Younger contrast (bottom).
Chapter 3 Figure 8
Note. Color bar (top) represents Z statistic. Orientation markers are displayed (A = Anterior, P =
Posterior, R = Right, L = Left). Younger N = 29, Older N = 28.

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Whole brain results for the Nostalgia > Unfamiliar Control contrast are presented in
Figure 9. Coordinates of peak clusters are presented in Table 6. Significant activation was
observed in regions overlapping those observed in the Nostalgia > Familiar Control contrast,
including the Default Mode Network (bilateral PCC, bilateral precuneus, left angular gyrus,
bilateral ACC, bilateral DMPFC and VMPFC), motor regions (bilateral SMA, bilateral precentral
gyrus), occipital regions (left superior lateral occipital cortex, left occipital pole), frontal regions
(bilateral frontal pole, bilateral SFG, left DLPFC, bilateral IFG, bilateral OFC), left MTG
(temporooccipital region), left SMG, bilateral anterior insula, bilateral VTA, superior colliculus,
bilateral caudate, bilateral thalamus, bilateral crus I, lobules VI, I-IV, and vermis X. Several
regions, not observed in the Nostalgia > Familiar Control contrast, showed additional activation:
left posterior MTG, left hippocampus, larger regions of left middle frontal gyrus (MFG) and left
IFG (pars triangularis).
Table 6.
Coordinates of peak clusters by region for the Nostalgia > Unfamiliar Control contrast, across
participants.
Chapter 3 Table 6
Area Z-value x y z
Frontal L ACC 4.73 -2 29 16
L anterior insula 5.58 -37 4 -1
L DLPFC 4.5 -33 48 27
L DMPFC 5 -6 28 43
L IFG (pars triangularis) 4.63 -52 30 11
L IFG (pars opercularis) 5.46 -48 11 4
L OFC 5.27 -40 31 -6

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L precentral gyrus 5.44 -49 -3 43
L SMA 5.66 -4 0 64
L VLPFC 4.93 -24 46 16
L VMPFC 5.57 -8 56 18
R ACC 4.92 4 34 5
R anterior insula 3.89 35 12 2
R DMPFC 3.17 8 20 46
R OFC 3.36 36 18 -15
R precentral gyrus 4.73 48 -7 42
R SFG 5.32 6 11 62
R SMA 5.33 3 4 60
R VMPFC 3.52 3 51 0
Temporal L hippocampus 3.52 -25 -24 -12
L MTG
(temporooccipital
regions)
4.97 -61 -52 1
L posterior MTG 4.96 54 -17 -14
Parietal L angular gyrus 4.5 -41 -54 35
L PCC 4.5 -6 -53 22
L precuneus 4.36 -9 -53 33
L SMG 4.4 -60 -42 36
R PCC 4 3 -50 15
R precuneus 3.16 5 -59 26
Occipital L occipital pole 3.72 -5 -92 -14
L superior lateral
occipital cortex
5.56 -32 -75 43
Subcortical L caudate 5.65 -18 14 13
L thalamus 5.32 -4 -3 7
L VTA 3.7 -4 -20 -17
R caudate 5.26 18 6 19
R thalamus 5.37 4 -2 5
R VTA 3.7 3 -18 -12

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Superior Colliculus 5.37 0 -30 -2
Cerebellar L crus I 4.86 -44 -59 -32
L I-IV 4.44 -2 -54 -5
L VI 5.93 -26 -60 -25
R crus I 4.73 39 -64 -26
R I-IV 3.96 2 -53 -6
R VI 5.91 28 -58 -28
Vermis VIIIa 3.5 2 -62 -32
Note. Coordinates are in MNI space. ACC = anterior cingulate cortex, DLPFC = dorsolateral
prefrontal cortex, DMPFC = dorsomedial prefrontal cortex, IFG = inferior frontal gyrus, OFC =
orbitalfrontal cortex, SMA = supplementary motor area, VLPFC = ventrolateral prefrontal
cortex, SFG = superior frontal gyrus, MTG = middle temporal gyrus, PCC = posterior cingulate
cortex, VTA = ventral tegmental area.

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Figure 9.
Whole brain results for Nostalgia > Unfamiliar Control contrast.
Chapter 3 Figure 9
Note. Color bar represents Z statistic. Total N = 57.
Between-group contrasts within the Nostalgia > Unfamiliar Control contrast revealed significant
activation in the Older > Younger age contrast (see Figure 10, top panel). Coordinates for peak
clusters are presented in Table 7. Specifically, activation for this contrast was observed in the left
angular gyrus, bilateral superior parietal lobule, and right precuneus. Further investigation of the
directionality of this effect indicated that, in the superior parietal lobule and precuneus clusters,
younger adults showed greater activation than older adults, particularly in the Nostalgia

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condition. In the left angular gyrus in the Nostalgia condition, older adults showed greater
activation than younger adults (see Figure 10, bottom panel).
Table 7.
Coordinates of peak clusters by region for the Nostalgia > Unfamiliar Control contrast, Older >
Younger.
Chapter 3 Table 7
Area Z-value x y z
L angular gyrus 4 -41 -52 25
R precuneus 3.5 1 -52 55
L SPL 4.04 -29 -43 72
R SPL 4.29 30 -43 74
Note. Coordinates are in MNI space. SPL = superior parietal lobule.

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Figure 10.
Whole brain results for Nostalgia > Unfamiliar Control, Older > Younger contrast (top). Bar
plots of younger and older adults percent signal change from rest in the Nostalgia and
Unfamiliar Control condition, within clusters significant in Nostalgia > Unfamiliar Control,
Older > Younger contrast (bottom).
Chapter 3 Figure 10
Note. Color bar represents Z statistic. Orientation markers are displayed (A = Anterior, P =
Posterior, R = Right, L = Left). Cluster 1 includes the left superior parietal lobule. Cluster 2

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includes the left angular gyrus Cluster 3 includes the left superior parietal lobule and left
precuneus. Younger N = 29, Older N = 28.
Whole brain results for contrasts of each condition vs rest (Nostalgia > Rest, Familiar Control >
Rest, Unfamiliar Control > Rest) are uploaded to Neurovault at
https://neurovault.org/collections/FEHSKFWF/.
ROI analysis
In region-of-interest analyses of percent signal change from rest (see Figure 11), we observed a
significant main effect of Condition on percent signal change from rest in MPFC (F(2, 330) =
3.69, p < 0.05, η2 = 0.07), indicating percent signal change from rest was higher in the Nostalgia
condition (M = 0.04, SD = 0.20) than the Unfamiliar Control (M = -0.07, SD = 0.20, p < 0.001)
and Familiar Control (M = -0.07, SD = 0.18, p < 0.001) conditions. In PMC, we observed a
main effect of Condition (F(2, 330) = 4.95, p < 0.01, η2 = 0.05), where percent signal change
from rest was higher in the Nostalgia condition (M = 0.02, SD = 0.25) than the Unfamiliar
Control (M = -0.07, SD = 0.23 p < 0.05) and Familiar Control (M = -0.09, SD = 0.22, p < 0.01)
conditions. In VTA, a main effect of Condition was observed (F(2, 330) = 3.40, p < 0.05, η2 =
0.04), where percent signal change from rest was higher in the Nostalgia condition (M = 0.09,
SD = 0.18) than the Unfamiliar Control (M = 0.03, SD = 0.16, p < 0.01) and Familiar Control (M
= 0.03, SD = 0.15, p < 0.05) conditions. A main effect of age group was also observed (F(2, 330)
= 4.15, p < 0.05, η2 = 0.10), where older adults (M = 0.10, SD = 0.14) had greater percent signal
change than younger adults (M = 0.003, SD = 0.17, p < 0.01) across conditions. No significant

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main or interaction effects were observed for MTL (ps > 0.05). No significant effect of
hemisphere and no interaction effects were observed for any ROI (ps > 0.05).

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Figure 11.
Mean percent signal change from rest between conditions and age groups in a priori-selected
regions of interest.
Chapter 3 Figure 11

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Note. MPFC = medial prefrontal cortex, PMC = posteromedial cortex, VTA = ventral striatum,
MTL = medial temporal lobe. Error bars represent standard error. No significant effects of
hemisphere were observed, thus bars represent mean percent signal change across both left and
right hemisphere for each ROI. Stars indicate statistical significance (* p < 0.05, ** p < 0.001,
*** p < 0.001). Younger N = 29, Older N = 28.
Individual Differences
Cognitive Ability
For the Nostalgia > Familiar Control contrast, MoCA did not predict activation across
participants. However, for the Nostalgia > Familiar Control, Younger > Older contrast, MoCA
score was associated with activation in the right lingual gyrus, right SFG, right frontal pole,
bilateral postcentral gyrus, right precentral gyrus, bilateral superior parietal lobule, right parietal
operculum, and right ACC (see Figure 12, top panel). Coordinates of peak clusters are presented
in Table 8. Investigation of the directionality of this effect was conducted (Figure 12, bottom
panel). Patterns across clusters demonstrated that, in younger adults, cognitive ability predicted
activation, such that higher cognitive ability was associated with greater activation in the
Nostalgia condition. In older adults, the relationship between cognitive ability and activation
during either condition was much flatter, in which cognitive ability did not strongly predict
activation in the nostalgia nor the Familiar Control condition. Results from the lower-level
contrasts (Nostalgia > Rest, Familiar Control > Rest) are uploaded to Neurovault at
https://neurovault.org/collections/FEHSKFWF/.

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Table 8.
Coordinates of peak clusters by region for the MoCA regressor, Nostalgia > Familiar Control
contrast, Younger > Older.
Chapter 3 Table 8
Area Z-value x y z
Frontal R ACC 3.66 7 -5 42
R frontal
pole
4.62 31 62 15
R
precentral
0.7 32 -8 52
R SFG 4.24 17 32 55
Parietal L
postcentral
3.8 -30 -39 62
L SPL 3.6 -31 -46 49
R parietal
operculum
4 42 -32 22
R
postcentral
4.36 32 -36 65
R SPL 3.7 36 -44 51
Occipital R lingual 4 17 -42 -6
Note. Coordinates are in MNI space. ACC = anterior cingulate cortex, SPL = superior parietal
lobule.

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Figure 12.
Whole brain results for Cognitive Ability regressor: Nostalgia > Familiar Control contrast,
Younger > Older adults (top) and line plots of younger and older adults percent signal change
from rest in the Nostalgia and Familiar Control conditions against cognitive ability score, within
clusters significant in Nostalgia > Familiar Control, Younger > Older contrast (bottom).
Chapter 3 Figure 12
Note. Color bar represents Z statistic. Results are shown averaged across clusters. MoCA scores
are mean-centered. Younger N = 29, Older N = 28.

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Trait Nostalgia
Trait Nostalgia did not predict activation in the Nostalgia > Familiar Control or Nostalgia > Rest
contrasts. In the Familiar Control > Rest, Older > Younger contrast, Trait Nostalgia was
associated with activation in the bilateral precuneus (see Figure 13, top panel). Coordinates of
peak clusters are presented in Table 9. In younger adults, higher Trait Nostalgia was associated
with decreased activation in the precuneus during Familiar Control music listening. This
relationship was much flatter in older adults but trended positively, with higher Trait Nostalgia
predicting greater activation (Figure 13, bottom panel). This result should be taken with caution,
however, as the only significant cluster appeared in a condition versus rest.
Table 9.
Coordinates of peak clusters by region for the Trait Nostalgia regressor, Control > Rest contrast,
Older > Younger.
Chapter 3 Table 9
Area Z-value x y z
L precuneus 3.6 -6 -65 38
R precuneus 3.34 5 -65 32
Note. Coordinates are in MNI space.

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Figure 13.
Whole brain results for Trait Nostalgia regressor: Familiar Control > Rest contrast, Older >
Younger adults (left) and line plots of younger and older adults percent signal change from rest
in the Familiar Control conditions against trait nostalgia score, within clusters significant in
Familiar Control > Rest, Older > Younger contrast (right).
Chapter 3 Figure 13
Note. Color bar represents Z statistic. Trait Nostalgia scores are mean-centered. Younger N = 29,
Older N = 28.

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Valence
In the Nostalgia > Familiar Control contrast across participants, positive felt valence across
nostalgic songs was associated with small clusters of activity in bilateral precuneus, right PCC,
and right caudate (see Figure 14). In the Nostalgia > Familiar Control, Older > Younger contrast,
positive valence ratings were additionally associated with activity in the bilateral cerebellum
(vermis VIIIa, bilateral VI, bilateral crus I, bilateral crus II), right PCC, and right precuneus
(Figure 15). Coordinates of peak clusters are presented in Table 10. Investigation of the
directionality of this effect revealed that, in the Nostalgia condition, older adults who felt more
positively while listening to nostalgic songs had greater activation than those who felt more
neutral. This pattern was not observed in the younger adult group.
Table 10.
Coordinates of peak clusters by region for the valence regressors, Nostalgia > Familiar Control
contrast, Older > Younger.
Chapter 3 Table 10
Positive Valence
Area Z-value x y z
Parietal R PCC 3.9 2 -50 17
R precuneus 4.9 14 -50 22
Cerebellar L crus I 3.8 -24 -82 -25
L crus II 3.8 -12 -76 -35
L VI 3.7 -7 -7 -26
R crus I 4.5 44 -68 -32
R crus II 3.4 8 -79 -30
R VI 3.5 16 -62 -21
Vermis VIIIa 3.56 -2 -62 -32
Negative Valence
Area Z-value x y z
Temporal R posterior MTG 3.8 59 -12 -10

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R anterior STG 4.14 49 -12 -3
Parietal L PCC 4.33 -2 -28 34
R PCC 4.8 4 -26 34
L precuneus 4.23 -11 -56 32
R precuneus 4.27 2 -62 28
Subcortical R thalamus 4.8 2 -14 5
Cerebellar L crus II 4.19 -18 89 -32
Note. Coordinates are in MNI space. MTG = middle temporal gyrus, STG = superior temporal
gyrus, PCC = posterior cingulate cortex.

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Figure 14.
Whole brain results for positive and negative valence regressors: Nostalgia > Familiar Control
contrast, all participants.
Chapter 3 Figure 14
Note. Color bar represents Z statistic. Younger N = 29, Older N = 28.

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For the negative valence model in the Nostalgia > Familiar Control contrast, negative
nostalgic song valence ratings were associated with small clusters of activity across participants
in the right PCC, bilateral precuneus, right caudate, right precentral gyrus, and right SMG (see
Figure 14). In the Nostalgia > Familiar Control, Older > Younger contrast, negative valence
ratings were additionally associated with activity in right posterior MTG, right anterior STG,
right thalamus, bilateral PCC, bilateral precuneus, and left cerebellum (crus II) (see Figure 15).
Coordinates of peak clusters are presented in Table 10. Investigation of the directionality of this
effect revealed that, in the Nostalgia condition, older adults who felt more negatively while
listening to nostalgic songs had greater activation than those who felt more neutral. This pattern
was not observed in the younger adult group.

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Figure 15.
Whole brain results for positive and negative valence regressors: Nostalgia > Familiar Control
contrast, Older > Younger adults (top) and line plots of younger and older adults percent signal
change from rest in the Familiar Control conditions against valence scores, within clusters
significant in Nostalgia > Familiar Control, Older > Younger contrast (bottom).
Chapter 3 Figure 15
Note. Color bar represents Z statistic. Orientation markers are displayed (A = Anterior, P =
Posterior, R = Right, L = Left). Percent signal change values are shown averaged across clusters.
Valence ratings are mean-centered. Younger N = 29, Older N = 28.

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Functional Connectivity
Posteromedial Cortex Seed
PPI results for the PMC seed are presented in Figure 16. In the Nostalgia > Rest condition
neither left nor right PMC significantly co-activated with other clusters. In the Nostalgia >
Familiar Control contrast, right PMC co-activated with the right anterior insula. In the Familiar
Control > Rest contrast, right and left PMC co-activated with left SMG. Coordinates of peak
clusters are presented in Table 11.
Table 11.
Coordinates of peak clusters by region for the PPI analyses.
Chapter 3 Table 11
Seed Contrast Area Z-value x y z
R PMC Nostalgia >
Familiar Control
R anterior
insula
4.3 36 30 40
Familiar Control >
Rest
L SMG 3.8 -60 -44 46
L PMC Familiar Control >
Rest
L SMG 4.38 -60 -44 46
L HG Nostalgia > Rest R PCC 3.9 2 -24 36
R angular 3.79 -48 -56 -48
L angular 3.16 -49 -56 46
R HG Nostalgia > Rest R angular 3.9 51 -54 52
L angular 4.5 -46 -54 49
L DLPFC 3.9 -44 25 44

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L frontal
pole
3.8 -44 42 7
L MTG 3.52 -66 -31 -4
R STG 4.8 66 -12 5
L crus I 3.8 -19 -81 -24
L crus II 3.8 -8 -86 -29
R crus I 4.3 19 -81 -24
R crus II 3.5 8 -86 -26
Familiar Control >
Rest
L STG 4.03 -58 -4 1
R STG 5.13 64 -10 1
R lateral
occipital
cortex
4.8 48 -69 -1
L
occipital
pole
4.15 -10 -100 7
R
occipital
pole
4.18 20 -100 7
L VI 3.9 -23 -56 -24
R VI 4.03 25 -56 -21
R crus I 4.42 21 -82 -23
L AG Nostalgia > Rest R angular
gyrus
4.3 -51 -58 44
L SMG 3.83 -58 -49 38
L
precuneus
5.08 -6 -70 38
R
precuneus
4.86 12 -70 39
L PCC 3.85 -5 -36 26
R PCC 4.31 5 -25 28
L insula 3.86 -40 14 -1
R insula 4.5 41 16 1

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Note. Coordinates are in MNI space. PMC = posteromedial cortex, SMG = supramarginal gyrus,
HG = Heschl’s gyrus, PCC = posterior cingulate cortex, DLPFC = dorsolateral prefrontal cortex,
MTG = middle temporal gyrus, STG = superior temporal gyrus, AG = angular gyrus.
Figure 16.
PPI results for Left and Right Posteromedial Cortex (PMC) seed, Nostalgia >Familiar Control
(top) and Familiar Control > Rest (bottom) contrasts.
Chapter 3 Figure 16
Note. Color bar represents Z statistic. Total N = 57.
Heschl’s Gyrus Seed

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PPI results for the Heschl’s gyrus seed are presented in Figure 17. Coordinates of peak clusters
are presented in Table 11. In the Nostalgia > Rest contrast, left Heschl’s gyrus co-activated with
right PCC and bilateral angular gyrus. No significant clusters were observed in the Familiar
Control > Rest contrast for left Heschl’s gyrus. Right Heschl’s gyrus, in the Nostalgia > Rest
contrast, co-activated with bilateral angular gyrus, left DLPFC, left frontal pole, left MTG, right
STG, and cerebellum (bilateral Crus I and II). In the Familiar Control > Rest contrast, right
Heschl’s gyrus co-activated with bilateral superior temporal gyri, right lateral occipital cortex,
bilateral occipital pole, and cerebellum (bilateral VI and right Crus I). See Figure 17 for left and
right Heschl’s gyrus PPI maps. No differences between age groups were observed in either
hemisphere or contrast, and no significant co-activation was observed for the Nostalgia >
Familiar Control contrast in either hemisphere.

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Figure 17.
PPI results for Heschl’s gyrus seed in Nostalgia > Rest (top) and Familiar Control > Rest
(bottom).
Chapter 3 Figure 17
Note. Color bar represents Z statistic. Total N = 57.

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Left Angular Gyrus Seed
Given the large cluster of activation in the left angular gyrus present in both the right and left
Heschl’s gyrus in the Nostalgia > Rest contrast, and the role of the angular gyrus in integrating
information within and outside of the default mode network (for review: Ramanan et al., 2018),
we conducted an a posteriori PPI analysis using the left angular gyrus as a seed within the
Nostalgia > Rest contrast alone. This seed was an 8-mm voxel sphere with a center-point at the
peak activation within the cluster observed in the Nostalgia > Rest right HG PPI result (-46, -52,
48). Co-activation with the left angular gyrus was observed in the right angular gyrus, left SMG,
bilateral precuneus, bilateral PCC, and bilateral insula (see Figure 18). Coordinates of peak
clusters are presented in Table 11. An overall visualization of nodes of functional connectivity
observed in this study is presented in Figure 19.
Figure 18.
PPI results for Left Angular Gyrus seed, Nostalgia > Rest contrast.
Chapter 3 Figure 18
Note. Color bar represents Z statistic. Total N = 57.

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Figure 19.
General PPI results in Nostalgia and Familiar Control conditions.
Chapter 3 Figure 19
Note. Locations are approximate for summarizing visualization purposes only. Created with
BioRender.com
Discussion
This study investigated the neural correlates of music-evoked nostalgia across the lifespan.
Healthy participants (29 younger adults and 28 older adults) listened to self-selected nostalgiaevoking pieces of music while undergoing functional MRI. Participants also listened to familiar
and unfamiliar non-nostalgic control songs, matched for musical and acoustic features to each of
the self-selected nostalgic songs. We observed two main findings across age groups: 1) Listening
to nostalgic music, more than familiar non-nostalgic or unfamiliar music, was associated with
bilateral activity in the default mode network, reward network, supplementary motor regions,
medial temporal lobe, and cerebellum. 2) Listening to nostalgic music involved functional
connectivity of Heschl’s gyrus (HG) with self-referential (angular gyrus, PMC) and affect-related

202
regions (insula). In contrast, non-nostalgic familiar music was associated with the co-activation
of HG with other feature-processing regions (i.e., STG, supramarginal gyrus). We observed two
findings between age groups: 1) Older adults demonstrated stronger recruitment of nostalgiarelated regions 2) While the neural response to nostalgic music in younger adults was modified
only by trait-level factors of dispositional nostalgia and cognitive ability, the response in older
adults was modified only by emotional response tendency (i.e., felt valence). We discuss each of
these findings, their relation to existing literature, and implications for future work below.
Self-selected music successfully evokes nostalgia
We first demonstrate that our method of asking participants to identify their own nostalgic music
was effective at evoking potent feelings of nostalgia in both age groups. Nostalgic songs were
rated as 8.4 and 8.6 out of 9 for younger and older adults, respectively. This supports previous
work from our group that self-selection is a highly effective method for identifying nostalgic
stimuli while accounting for the heterogenous nature of music habits, preferences, and
experiences (i.e., Hennessy et al., in press; Greer et al., under review). Similarly, our method of
identifying both familiar and unfamiliar non-nostalgic songs to create musically-matched stimuli
triplets was also successful. These songs were adequately matched on acoustic and musical
features that may otherwise contribute to neural activation patterns and allowed us to
systematically separate the effect of nostalgia from the effect of musical style preference or
familiarity. We encourage researchers to utilize this method for future work on music-evoked
emotion and autobiographical memories, using our freely available online matching tool
(soundslikethis.com).

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Music-evoked nostalgia activates DMN, MTL, and reward regions
In support of our hypothesis, we observed that nostalgic music, compared to musically-matched,
familiar, but non-nostalgic music, was associated with activation in the default mode network,
medial temporal lobe, and reward regions. We saw additional activation in the salience network
(ACC, insula), regions implicated in emotion regulation (ACC, DLPFC), feature processing
regions (SMG), motor regions, visual areas, and cerebellum. In comparison to musicallymatched unfamiliar music, nostalgic music listening was associated with similar and larger
regions of activation, with additional activation in areas implicated in musical familiarity (i.e.,
MTG and pars triangularis; Vuong et al., 2023). In a priori-selected region of interest, percent
signal change from rest was significantly greater in the nostalgia condition than in non-nostalgic
conditions in the MPFC, PMC, and VTA.
These findings are in line with previous work examining picture- and music-evoked
nostalgia, in which activity was observed in reward regions (ventral tegmental area) (Oba et al.,
2016; Trost et al., 2012), SMA (Oba et al., 2016), cerebellum (Oba et al., 2016), thalamus (Oba
et al., 2016), SMG (Zhang et al., 2022), OFC (Trost et al., 2012; Zhang et al., 2022), and lateral
occipital cortex (Zhang et al., 2022). Our large cluster of activity encompassing the dorsomedial
and ventromedial prefrontal cortex was additionally consistent with work examining odorevoked nostalgia (Matsunaga et al., 2013), music-evoked nostalgia (Trost et al., 2012) and
music-evoked autobiographical memory (Janata, 2009; Trost et al., 2012). Additionally, our
observed finding in the insula and reward regions (VTA and substantia nigra) is in line with
Barrett and Janata’s (2016) finding that these regions tracked the tonal structure of nostalgic
music. Moreover, the fact that these regions were observed in the nostalgic vs. non-nostalgic

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familiar music contrast suggests that music-evoked nostalgia is associated with neural activation
above and beyond what can be accounted for by familiarity or musical feature preference.
With these results, we provide evidence for a true nostalgia-related brain network that
mirrors the psychological and social functions of the emotion (Yang et al., 2022, 2023). First,
observed activation in self-referential regions of the brain (precuneus, PCC, MPFC), mirrors
nostalgia’s inherent nature as a self-salient and self-reflective emotion (Sedikides et al., 2016;
Sedikides, Wildschut, Gaertner, et al., 2008). Second, observed activation in regions implicated
in autobiographical processing (parahippocampal gyrus, temporal pole, PCC, MPFC, angular
gyrus) reflect nostalgia’s role in autobiographical thought and ties to past events (Sedikides et al.,
2015b; Wildschut et al., 2006). Third, we observed activity in regions implicated in emotion
regulation (ACC, DLPFC), mirroring the regulatory role of nostalgia to ameliorate negative
emotion (Wildschut & Sedikides, 2023). Finally, we observed activity in a distributed network of
reward-related regions (VTA/SN, MPFC, caudate, thalamus), aligning with nostalgia’s place as a
mostly positive-valenced emotion (Leunissen et al., 2021; Sedikides et al., 2015; Wildschut et
al., 2006) that facilitates approach motivation (Stephan et al., 2014). Overall, the present findings
support proposed neural models of “the nostalgic brain” as one that is implicated in selfreferential processing and autobiographical memory (both of which encompass the majority of
the default mode network), emotion regulation, and reward.
In addition to these hypothesized regions, we observed activation in regions of the brain
previously observed in general music-evoked emotion research, including sensory and motor
regions (Koelsch, 2020). The observed activation in occipital regions (lateral occipital cortex,
intracalcarine), additionally aligns with past work on music-evoked emotions (Belden et al.,
2023; Sachs et al., 2020; Trost et al., 2012) and autobiographical memory retrieval (Ferris et al.,

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2024; Spreng et al., 2009; Summerfield et al., 2009). Participants were asked to keep their eyes
closed during the entirety of the fMRI scan and, although we did not monitor adherence to this
instruction with eye blink software, we believe it is unlikely that participants systematically kept
their eyes open more for the nostalgic listening than other listening conditions. Instead, we
interpret this finding as involvement of mental imagery during nostalgic listening.
Contrary to our hypothesis, we did not observe a greater percent-signal change in
nostalgic listening in the medial temporal lobe ROI, obtained from Kim (2012)’s meta-analysis
on autobiographical memory. We did, however, observe small clusters of activity in regions of
the medial temporal lobe, specifically bilateral posterior parahippocampal gyrus, during our
whole-brain analysis, which was located dorsal to our MTL seed. In his study on music-evoked
autobiographical memory, Janata (2009) observed that medial temporal lobe activity did not
correlate with autobiographical salience. This was interpreted as the result of differences between
passive listening (as done in Janata (2009)) and effortful retrieval (as done in previous
autobiographical memory imaging work). In a later study, this interpretation was supported, in
which the authors observed that MTL was involved in music-evoked autobiographical memory
processing only when participants were explicitly instructed to attend to the memories listening
(Kubit & Janata, 2018). Here, participants engaged in passive listening only. Thus, the
engagement of even small clusters of the hippocampal regions is notable. This finding suggests
that nostalgic music’s affective quality may help participants transition into a more memoryattentive neural state. Additionally, passively listening to nostalgic music appears to engage a
more a dorsal region of the MTL as compared to regions implicated in active autobiographical
memory retrieval.

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DMN and auditory integration during nostalgic listening
Functional connectivity findings indicated that listening to nostalgic music involved coactivation of Heschl’s gyrus (HG) with self-referential (angular gyrus, PMC) regions and anterior
insula. In contrast, non-nostalgic music was associated with co-activation of HG with other
feature-processing regions (i.e., STG, supramarginal gyrus). These results are partially aligned
with our hypotheses, in which we predicted that functional connectivity would be greater during
nostalgic music listening between default mode network, auditory cortices, and reward regions.
Notably, during nostalgic listening, we only observed connection between auditory and
posteromedial cortex when examining connectivity via the left angular gyrus. In other words, we
did not find that Heschl’s Gyrus co-activated with PMC alone- it only co-activated with the left
angular gyrus, which in turn co-activated with PMC and anterior insula. The angular gyrus
serves as a structural (Frey et al., 2008; Makris et al., 2005, 2009; for review: Seghier, 2013) and
functional (Bonnici et al., 2016; Ramanan et al., 2018) hub for sensory integration and higherorder cognition, including autobiographical memory. It is implicated in autonoetic awareness
(Zaman & Russell, 2022) and perceptually rich autobiographical thought (Simons et al., 2008,
2010; Tibon et al., 2019), supported by its location as a structural convergence zone between
sensory, default mode, and autobiographical memory regions (Binder & Desai, 2011; Seghier,
2013). Our finding suggests during nostalgic music listening, the angular gyrus integrates lowerlevel auditory information from Heschl’s Gyrus to regions implicated in self-referential
processing (PMC) and affect or salience (anterior insula; Gasquoine, 2014; Menon & Uddin,
2010; Uddin et al., 2017). During familiar non-nostalgic music listening, both auditory cortex
and posteromedial cortex instead co-activate with other regions implicated in extracting feature
information from a stimulus (HG to STG (Janata, 2015) and occipital cortex (Malach et al.,

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1995; Satoh et al., 2011), and PMC to SMG (Celsis et al., 1999; Oberhuber et al., 2016; Schaal et
al., 2015, 2017)). The nostalgic quality of a musical piece, therefore, appears to drive integration
beyond lower-level feature processing and into higher-order meaning-making networks. These
findings are conceptually consistent with previous work; for example, Kubit and Janata (2018)
observed that attending to memories for music was associated with greater integration of left
angular gyrus, auditory (MTG), and posterior default mode (PCC). This is again notable given
that participants were passively listening to songs and not explicitly instructed to attend to
memories.
Contrary to our hypothesis, we did not see co-activation with reward regions from either
of our PPI seeds. Previous work has observed greater auditory-to-reward connectivity during
well-liked familiar music listening (Belden et al., 2023), and auditory-to-reward connectivity
scaling positively with aesthetic pleasure from music (Salimpoor et al., 2013). Functional
connectivity measures vary widely, however, and differences between the present result and
previous work could be a product of our choice of a more conservative method of functional
connectivity (seed-based PPI), as compared to the methods utilized in Belden and colleagues
(2023). In their analysis, multiple regions were combined across each network before averaging
timeseries across the network of interest. and analyses were constrained between the individual
networks of interest, rather than searching across the entire brain. These methodological
differences may account for the lack of auditory-reward findings in the present study, but future
work is needed to investigate this phenomenon further.

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Age-related findings
Older adults have stronger activation in nostalgia-related regions during listening
We observed several age-related findings when examining the neural correlates of music-evoked
nostalgia. First, across all music conditions, older adults showed greater VTA activation than
younger adults. This is broadly consistent with the age-related “positivity effect” (Mather &
Carstensen, 2005) in which older adults have a bias towards attending to positive stimuli and
away from negative stimuli (Reed et al., 2014). This bias manifests in many ways, including a
greater prevalence of positive emotion and greater memory for positive versus negative events
(Sakaki et al., 2019), and is thought to reflect the shifting goals and motivations associated with
aging (Kennedy et al., 2004; Mather & Carstensen, 2005). In the present study, we report
evidence of this effect in behavioral valence ratings, in which older adults reported feeling more
positive than younger adults, regardless of song condition. This positivity bias may thus be
reflected in overall increased VTA activity, as VTA is implicated in reward (Lammel et al., 2012)
and musical pleasure (Blood & Zatorre, 2001; Menon & Levitin, 2005; Salimpoor & Zatorre,
2013)
Additionally, when listening to nostalgic music in comparison to familiar non-nostalgic
music, older adults had greater activation than younger adults in several regions involved in
nostalgic listening: temporal regions (STG, planum polare, MTG, temporal pole), somatosensory
regions (right postcentral gyrus), and the left angular gyrus. In comparison to unfamiliar music,
older adults also showed greater activity in nostalgia-related regions, including precuneus and
angular gyrus, and less deactivation in the superior parietal lobule. This finding is consistent with
evidence that older adults over-activate task-relevant neural regions during emotion (Kehoe et
al., 2013) and memory tasks (Cabeza et al., 2002; Galdo-Alvarez et al., 2009) and fail to fully

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de-activate task-irrelevant regions (Gordon et al., 2014; Lustig et al., 2003; Persson et al., 2007).
Here, the superior parietal lobule, a region involved in top-down processing of information
(Shomstein, 2012) and reciprocally deactivated with DMN (Nakano et al., 2013), is considered
task-irrelevant.
These findings can be interpreted in one of two ways. First, older adults may over-recruit
and fail to fully de-activate as a way of compensating for declining efficiency in other neural
regions involved in nostalgic listening. This explanation is in line with the Compensation Related
Utilization of Neural Circuits Hypothesis (CRUNCH; Reuter-Lorenz & Lustig, 2005),
suggesting that age-related structural decline and processing inefficiencies force older adults to
compensate by over-recruiting less-affected regions, showing larger response in task-related
regions. Alternatively, over-recruitment, particularly involving default mode regions, may reflect
healthy age-related shifts in self-referential thought and autobiographical memory retrieval
priorities that ultimately promote emotional well-being and social connectedness (AndrewsHanna et al., 2019; Grilli & Sheldon, 2022). These shifts might lead to consistently increased
neural recruitment during tasks that involve self-referential and autobiographical memory, like
nostalgic music listening.
Age-related stabilization of trait-level measures on neural activation of nostalgia
In our investigation of the role of individual differences on nostalgia-related neural activity, we
observed that trait-level measures of cognitive ability and dispositional nostalgia predicted neural
activity for younger adults but not older adults. Specifically, less nostalgic younger adults
appeared to have more activity in a key nostalgia-related region (precuneus) while listening to
non-nostalgic songs. Barrett and Janata (2016) observed that younger adults who had lower trait
nostalgia had greater activity associated with nostalgia ratings during music listening in SFG,

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temporal pole, and reward regions. Here, however, we see activity in a key node of selfreferential processing implicated in nostalgia across analyses, active during non-nostalgic music.
This suggests that younger adults who routinely experience nostalgia may have a greater
differentiation between nostalgic and non-nostalgic brain states during music listening than those
who experience nostalgia less frequently. In contrast, older adults’ neural responses to music do
not appear to be impacted by trait-level nostalgia. Previous behavioral evidence points to an agerelated decline in the relationship between episodic memory and the personality dimension of
neuroticism (Steenhaut et al., 2018), which is linked to trait nostalgia (Barrett et al., 2010;
Hennessy et al., in press). Our results similarly suggest that older adults’ neural response to
autobiographically salient music is robust to individual differences in personality. However, this
effect warrants further exploration.
Similarly, while older adults' nostalgia-related neural activity was not impacted by
cognitive ability, younger adults with higher cognitive ability had greater activation during
nostalgic listening in several sensory and motor regions implicated in nostalgia processing.
Overall, this finding suggests that cognitive ability's influence on neural response to nostalgic
music stabilizes with age. While this may be promising due to its implications for the
preservation of music-evoked emotions and memories in individuals with cognitive decline,
these findings are taken with caution. Participants in this study were intentionally screened to
keep the sample in the healthy range of cognitive scores, and thus, the range of scores present is
extremely limited (26-30 out of 30). Future research is needed with a larger range of cognitive
scores to appropriately assess whether the neural correlates of music-evoked nostalgia are robust
to cognitive decline.

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Positive and negative felt valence predict neural activity in older adults
We observed that, across age groups, average positive and negative felt valence were both
associated with activity in self-related (precuneus, PCC) and reward regions (caudate) during
nostalgic music listening. Negative valence was associated with additional activity in motor
(precentral gyrus) and feature processing (SMG) regions. Previous findings have shown similar
regions implicated in valence experienced from music (Koelsch et al., 2006; Trost et al., 2012)
valence of self-directed thoughts during music listening (Koelsch et al., 2022), and valence
associated with autobiographical memories (Speer et al., 2014). However, it is important to note
that our finding does not imply that activity in these regions correlates with the valence of an
individual stimulus. We retrieved an average valence response across nostalgic songs for each
participant, creating a measure that reflects an individual’s tendency to feel more positive or
negative when listening to nostalgic music (i.e., “positive feelers” and “negative feelers”).
Unlike other trait measures, younger and older adults differed on how valence impacted
their neural response to nostalgic music. Specifically, neural activation associated with positive
valence appeared to be driven by the older adult group. In the older adult group, individuals who
tended to feel more positively when listening to nostalgic music had greater activation than those
who felt more neutral in self-related regions (PCC and precuneus) as well as the cerebellum.
Similarly, the effect of negative valence was driven by the older adult group, such that older
adults who felt more negatively had greater activation than those who felt more neutral during
nostalgic songs in auditory (MTG, STG), self-referential (precuneus, PCC), thalamus, and
cerebellum.
While the increased activation in positive feelers may be explained by age-related
positivity effects, overlapping and additional activation for negative feelers complicates this

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interpretation. Additionally, an overwhelming number of studies observe that aging is associated
with increased reliance on prefrontal regions during emotion processing (for review: Nashiro et
al., 2011), as opposed to posterior and temporal regions observed here. It is conceivable that the
processing of music-evoked emotions undergoes distinct age-related shifts in neural activation
compared to responses to other stimuli. Unlike images and words often used in emotion
regulation studies, music is a dynamic stimulus whose emotion-inducing properties unfold over
time. Thus, music- particularly nostalgic music- may be able to better maintain attention toward
an emotion because the nature of the experience does not allow for attentional redirection. In the
face of this immersive stimulus, it may be that older adults are more willing to engage with both
positive and negative feelings without actively regulating them (as might be reflected in
increased prefrontal activity; Golkar et al., 2012). This finding is consistent with our overall
observation that older adults tended to over-recruit task-relevant regions in our general wholebrain analyses.
No age-related differences in functional connectivity
We did not observe age-related differences in functional connectivity during nostalgic or
control listening. Again, this contrasts with Belden et al. (2023), who observed more diffuse
connectivity patterns in older compared to younger adults during music listening. Our results
could suggest that functional connectivity patterns supporting the experience of nostalgia stay
consistent across the lifespan. However, given the presence of age-related findings in the general
activation patterns for each condition, we are hesitant to make this claim. Instead, divergence
from Belden et al.'s (2023) findings may simply be due to methodological differences, as noted
in the previous discussion of functional connectivity.

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Limitations and Future Directions
We note several limitations in this study. First, this cross-sectional investigation cannot draw
temporal inferences about music-evoked nostalgia in aging. It could be that age-related effects
observed in this study result from generational differences in how individuals respond to music
or experience nostalgia. Longitudinal research is required to fully investigate the effect of age on
the neural representation of nostalgia as evoked by music.
Secondly, we acknowledge that, while studying a dynamic stimulus such as music,
choosing which segment of the piece to play may greatly impact findings. In this study, we used
the first 40 seconds of each song for consistency. However, for some pieces of music, it is likely
that the first 40 seconds may not have included the chorus, which may have been the most
recognizable or potentially most nostalgic part of the piece. However, behavioral research has
demonstrated that individuals can just familiarity and liking a song within the first 750
milliseconds (Belfi, Kasdan, et al., 2018), suggesting that the emotional qualities of a piece of
music may be evoked early on in a piece of music. Additionally, given that we did observe
highly significant clusters of activation for our contrasts of interest using the beginning portion
of each song, we contend that this was an adequate choice. Future behavioral work could
examine the relative nostalgic quality of different structural elements of nostalgic music (i.e.,
verse, chorus, bridge). Future neuroimaging work could show how neural activity relates to
dynamic aspects of full-length nostalgic songs using naturalistic methods and analyses like
intersubject correlation (i.e., Sachs et al., 2020) and hidden Markov modeling (i.e., Williams et
al., 2022).
Finally, our study differs from previous research in that we did not obtain ratings of
pleasantness or enjoyment from participants for each piece of music. Previous investigations

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have used preference ratings as a primary regressor in their analyses, which was not done here.
While it is reasonable to assume that nostalgic music might have been rated as preferred to nonnostalgic music, we do not see this as a necessary limitation. From valence ratings, we observed
that participants felt more positive while listening to nostalgic music compared to familiar and
unfamiliar non-nostalgic music, which is in line with the affective experience of nostalgia. To
assess the emotional construct of nostalgia, we expected that these ratings would differ between
conditions, and we see this as a separate construct from enjoyment. Given the stimulus matching
procedure, we feel confident that enjoyment was matched across song conditions. Preferences for
specific acoustic and musical features predict enjoyment of music (Barone et al., 2017; Rentfrow
et al., 2011); thus, our steps to match these features should control for this enjoyment aspect. We
see this matching as a considerable strength of our experimental design.
Constraints on Generality
Several factors in this study constrain our ability to generalize findings. Most notably, our sample
is not representative of the population of adults in the United States. Most of our younger adult
sample was recruited from the University of Southern California’s undergraduate population,
which is skewed to be wealthier (Opportunity Insights, 2018) and more educated than the general
U.S. population. The older adult sample was recruited from the greater Los Angeles area, whose
demographics mirror more closely those of the U.S. (U.S. Census Bureau, 2020). However, we
did not collect race or ethnicity information from our participants and, therefore, cannot claim
that our sample was racially or ethnically representative. Future research should employ a
statistically representative sample of both younger and older adults. This is particularly important
given this work’s implications for Alzheimer’s Disease and Related Dementias, as ADRD

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disproportionately affects people of color (Lennon et al., 2022). Yet, people of color are
historically underrepresented in aging and dementia research (Nápoles & Chadiha, 2011).
Conclusions
This is the first study to explore how self-selected nostalgic music affects neural activity in
younger and older adults. We demonstrate that music-evoked nostalgia is supported by neural
activation and functional connectivity patterns that differ from musical familiarity and are not
attributable to acoustic features or musical style. These patterns involve brain regions implicated
in self-referential processing, autobiographical memory, reward, and emotion regulation. We
demonstrate that these patterns are consistent in location across the lifespan, but older adults
show stronger recruitment of several nostalgia-related regions. Lastly, we show that neural
correlates of nostalgic music listening stabilize across the lifespan regarding person-level
characteristics of nostalgia-proneness and cognitive ability and become more variable with
differences in affective tendencies. This study underscores the use of personalized stimuli in
investigating music-evoked emotions and highlights the preservation of neural resources during
nostalgic music listening in aging. This work may serve as a healthy baseline for future studies
examining the neural correlates of music-evoked nostalgia in individuals with Alzheimer’s
Disease and Related Dementias.

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Chapter 4: “We read the news today, oh boy”: intersubject neural synchronization during
nostalgic music listening in older adults
Abstract
Nostalgia is a mixed emotion with self and social functions. Music is a powerful nostalgia trigger
and has been used to investigate this emotion's affective signature and neural basis. However,
previous work is limited in its use of shorter musical pieces due to fMRI methodological
constraints, which reduces ecological validity. Additionally, limited work has examined the
neural underpinnings of nostalgia in older adults. In this study, twenty-seven healthy older adults
(aged 61-79) listened to the full-length version of a nostalgic song: “A Day in the Life” by The
Beatles while undergoing fMRI scanning. Activity in the auditory cortex, motor regions, reward
regions, default mode network, and autobiographical memory network were synchronized across
participants, time-locked to the nostalgic song. Neither state- nor trait-level nostalgia
significantly modulated inter-subject neural synchrony. The results suggest that listening to
nostalgic music involves auditory and motor regions and regions implicated in self-referential
processing, autobiographical memory, and reward. Moreover, these findings are robust to
individual differences in nostalgia responding in older adults.

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Introduction
Nostalgia, a sentimental longing for the past, is a universally experienced emotion traversing
ages (Hanson et al., 2022) and continents (Hanson et al., 2022; Hepper et al., 2024; Saarikallio et
al., 2020). Nostalgia’s affective profile is that of a mixed emotion, comprised of a blend of
primarily positive valence (Hepper et al., 2012; Leunissen et al., 2021; Sedikides et al., 2015;
Wildschut et al., 2006) tinged with elements of melancholy (Hepper et al., 2012; Holak &
Havlena, 1998; Turner & Stanley, 2021). Nostalgia is a social and self-relevant emotion (Ismail
et al., 2022; Madoglou et al., 2017; Wildschut et al., 2006), adaptively serving to fortify personal
identity (Hong et al., 2021; Vess et al., 2012) and foster a sense of belonging (Cheung et al.,
2016; Juhl & Biskas, 2023). Nostalgia is often measured on a situational basis but holds traitlevel components, as individuals vary in the degree to which they experience and value nostalgia
in their daily lives (Sedikides et al., 2015; Wildschut et al., 2023). Our understanding of the
neural basis of this emotion is growing but still limited. In particular, it is unclear whether
individuals recruit similar neural networks as a naturalistic experience of nostalgia unfolds over
time. Notably, music, particularly from formative years in adolescence and early adulthood
(Jakubowski et al., 2020; Krumhansl & Zupnick, 2013), emerges as a potent nostalgia trigger
(Barrett et al., 2010; Hennessy et al., in press; Jakubowski, 2019). Music's dynamic, temporal
nature makes it a compelling medium for investigating this complex emotion as it evolves.
Decades of research have utilized music to understand human emotions (for review:
Schaefer, 2017). Functional magnetic resonance imaging (fMRI) studies observe that musicevoked emotions recruit many of the same neural regions implicated in non-music-evoked
emotions (Koelsch, 2015, 2020). Music-evoked nostalgia additionally activates regions involved
in autobiographical memory, self-referential processing, and reward, including the medial

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temporal lobe (Trost et al., 2012), medial prefrontal cortex (Trost et al., 2012), and ventral
tegmental area (Barrett & Janata, 2016). Nostalgia, as evoked by other stimuli like odor
(Matsunaga et al., 2013) and images (Oba et al., 2015; Zhang et al., 2022), similarly shows the
recruitment of memory and reward regions.
A limitation of this work is that many neuroimaging methodologies do not allow
researchers to take advantage of the time-varying aspects of music, limiting ecological validity.
For example, most fMRI designs require relatively short stimuli to model the hemodynamic
response. Thus, they do not necessarily reflect a naturalistic listening experience. One method
that allows for naturalistic music listening in fMRI studies is to examine the degree to which the
time-varying neural activity correlates across participants for the scan’s duration (inter-subject
correlation; ISC) (Hasson et al., 2010). Unlike other fMRI analyses, ISC is model-free; it does
not apply an expected hemodynamic response model to data. Instead, ISC allows researchers to
examine how participants’ synchronized neural responses vary with time-locked aspects of a
stimulus.
Previous fMRI work has used ISC to investigate neural synchrony across participants
during music listening (Abrams et al., 2013; Alluri et al., 2013; Regev et al., 2021; Sachs et al.,
2020; Trost et al., 2015). Abrams and colleagues (2013) conducted the first investigation of
natural music listening using ISC. They observed that symphonic music, compared to scrambled,
pseudomusical stimuli, synchronized neural activity across participants in auditory regions, rightlateralized frontal and parietal regions, motor regions, and midbrain regions (Abrams et al.,
2013). Others have similarly shown intersubject synchronization of auditory, motor, and
subcortical areas during music listening (Alluri et al., 2013; Regev et al., 2021; Sachs et al.,
2020; Trost et al., 2015) and silent musical imagery (Regev et al., 2021). In an investigation of

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neural synchrony predicted by ratings of emotional experience, Trost and colleagues (2015)
showed that arousal predicted activity in the auditory cortex and subcortical regions and valence
predicted de-activation in the left-lateralized auditory cortex, anterior cingulate, post-central
gyrus, and subcortical areas. Sachs and colleagues’ 2020 study expanded upon these findings,
examining how moment-to-moment ratings of sadness and enjoyment predicted dynamic
intersubject synchrony while listening to a sad piece of music (Sachs et al., 2020). They revealed
that synchrony in auditory, visual, and prefrontal regions was greater in individuals scoring
higher in a trait-level facet of empathy specifically tied to emotional immersion. In assessments
of continuous emotion measurements, the authors found that these ratings predicted dynamic ISC
in auditory regions and the default mode, orbitofrontal, and striatal networks (Sachs et al., 2020).
These studies provide a solid foundation for stimulus-driven neural patterns during music
listening and how basic emotions impact these patterns. However, no study to our knowledge has
used ISC to investigate the multidimensional emotion of nostalgia or to examine music-evoked
emotions in older adults. As our global population ages (He et al., 2016), it is increasingly
important for research to include diverse age groups beyond the predominantly studied younger
adults in neuroimaging emotion research. Emotional experience changes with age (Mather, 2012;
Mather & Carstensen, 2005); thus, age diversity in emotion research is needed. Finally, musicbased interventions are a growing tool in treating age-related neurodegenerative diseases
(Jennings & Vance, 2002; Brancatisano et al., 2020; Gómez-Gallego et al., 2021; Leggieri et al.,
2018; Lineweaver et al., 2021; Lord & Garner, 1993). Understanding music-evoked emotional
processes in healthy older adults can help provide context for future research and application.

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This study aims to expand on existing work investigating emotion during naturalistic music
listening. We assess regions of neural synchrony implicated in a shared nostalgic music-listening
experience among older adults. We will additionally examine whether the level of felt nostalgia
(state nostalgia) or the dispositional tendency to feel nostalgic in daily life (trait nostalgia)
modulates this neural synchrony. We hypothesize that 1) activity in known auditory and music
processing regions (Heschl’s Gyrus, superior temporal gyrus, inferior frontal gyrus, motor
regions) will significantly correlate across participants, 2) higher state nostalgia will additionally
predict synchrony in the default mode network (precuneus, posterior cingulate, medial prefrontal
cortex, angular gyrus; known to be involved in self-referential processing), medial temporal lobe
(involved in autobiographical memory), and reward regions (VTA/SN). Our hypothesis related to
trait nostalgia remains exploratory. Previous research has shown that trait nostalgia positively
predicts the relationship between state nostalgia and reward activity (Barrett & Janata, 2016).
However, our group demonstrated that only younger, not older, adults had trait nostalgia-related
modifications of neural activity. It remains to be seen how trait nostalgia predicts synchrony
among older adults during nostalgic listening.
Methods
The Institutional Review Board of the University of Southern California (IRBUP: UP-22-00569)
approved all procedures. This study and its hypotheses were not pre-registered. All materials,
including data and analysis code, are available online at https://osf.io/ahtfm/.
Participants
We recruited 30 right-handed English-speaking older adults from the Los Angeles area.
Inclusion criteria: 1) fluent in English, as determined by self-report and ability to complete prescreening questionnaire without difficulty; 2) have no contra-indication to participate in an MRI

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study (i.e., no irremovable metal implants, pacemakers, etc.), 3) right-handed, to reduce fMRI
data variability due to differences in handedness, 4) not currently experiencing psychiatric
symptoms by self-report, and 5) no history of neurologic disorders, 6) a score of 26 or above on
the Montreal Cognitive Assessment (MoCA; Nasreddine et al., 2005) to ensure that participants
did not have evidence of Mild Cognitive Impairment, 7) age 60 or older.
Participants were recruited from online research platforms and Facebook advertisements
from November 2022 to March 2023 as part of a more extensive study on music-evoked
nostalgia across the lifespan (see Hennessy et al., in preparation). Three of the thirty participants
who underwent MRI scanning were excluded from the final analysis: 2 due to incomplete data
and 1 due to unfamiliarity with the music stimulus (see below). This resulted in a final sample
size of 27 older adults aged 61-79. See Table 1 for demographic characteristics.
Table 1.
Participant demographic characteristics.
Chapter 4 Table 1
Mean (SD)
N 27
Gender
Female (%) 40.74
Male (%) 59.26
Nonbinary (%) 0
Age 68.11 (4.84)
Musicianship
Music training score 13.85 (9.21)

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Plays instrument (%) 77.78
Age at training onset 10.10 (3.3)
Absolute pitch (%) 0
MoCA Score 27.74 (1.35)
Trait Nostalgia 4.57 (1.12)
Procedure
Participants completed screening Informed Consent and then were screened for eligibility using
REDCap (Harris et al., 2009, 2019). REDCap (Research Electronic Data Capture) is a secure,
web-based software platform designed to support data capture for research studies, providing 1)
an intuitive interface for validated data capture; 2) audit trails for tracking data manipulation and
export procedures; 3) automated export procedures for seamless data downloads to common
statistical packages; and 4) procedures for data integration and interoperability with external
sources. In the form, participants reported their age, gender, history of neurologic, psychiatric,
and vascular disorders, history of traumatic brain injury, English fluency, and handedness. If
initially eligible, participants were contacted to complete an MRI safety screening form, which
included questions related to MRI contraindications (i.e., metal implants, presence of
pacemaker). After screening, they were invited for a 1-hour virtual visit via Zoom (Zoom.us),
during which they gave full informed consent, and the Montreal Cognitive Assessment was
administered to assess the presence of Mild Cognitive Impairment. If participants received a total
score of 26 out of 30 or higher, they were invited to participate in the remainder of the study. If
participants received a total score lower than 26, they were notified that they were ineligible for
the remainder of the study. They were encouraged to seek additional testing and referred to

223
resources at USC (adrc.usc.edu) and the Alzheimer’s Association (alz.org). Participants who
were ineligible at this stage received $20 for their participation in the Zoom visit.
Eligible participants then completed a one-hour survey containing music listening
activities (the results reported in Hennessy et al. (in preparation)), and trait-level assessments.
Participants were invited for an in-person visit to the University of Southern California’s Dana
and David Dornsife Neuroimaging Institute, where they underwent structural, functional, and
diffusion-weighted MRI imaging. Diffusion-weighted imaging results will be reported in a future
manuscript. Participants participated in an autobiographical memory task directly after the scan
and again one week later via Zoom (the results of which will be reported in a future manuscript).
Participants were thanked for their time and given $60 in compensation.
Materials
Montreal Cognitive Assessment
The Montreal Cognitive Assessment (MoCA; Nasreddine et al., 2005), was used to screen
participants for Mild Cognitive Impairment (MCI). This task was administered online via Zoom
with a MoCA-certified researcher. The MoCA includes language, memory, attention, visuospatial
skills, orientation, and mental calculation measures. Several amendments were made to
accommodate the videoconferencing platform. First, during the Alternating Trail Making task,
we altered the task by asking participants to use their mouse or finger to draw on a shared screen
using Zoom's “Annotate” function. Secondly, during the Cube and Clock drawing tasks,
participants were asked to complete their drawings on a piece of physical paper and then to hold
their illustration to the camera so that the researcher could take a screenshot of the drawing.
Third, during the Attention task, in which the experimenter read a list of letters and asked the
participant to clap on the letter “A,” participants were asked to hold their hands to the camera to

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be visible as they clapped. Due to the slight delay caused by the virtual platform, experimenters
were trained to watch and listen for the participant’s clap during the letter directly following the
target letter, approximately one second after the prompt. We chose to use these methods on the
full version of the MoCA rather than using the telephone version (T-MoCA) to enable the
completion of all portions of the cognitive assessment (T-MoCA excludes all drawing sections).
We piloted these methods in a group of older adults for feasibility, and they were found to be
easy to complete for this age cohort.
Musician Status
During the online survey, we collected information about participants’ experience playing music
for descriptive purposes only. These questions contained items from the Music Training subsection of the Goldsmith Music Sophistication Index (Müllensiefen et al., 2014). Scores were
summed to create a “music training” aggregate score, and individual elements were kept in their
raw form for descriptive purposes (i.e., “Do you or have you ever played an instrument?”), and
are reported in Table 1.
Trait Nostalgia
To assess trait-level nostalgia during the online survey, we administered the 7-item version of the
Southhampton Nostalgia Scale (SNS; (Sedikides et al., 2015). This scale includes items that
measure nostalgia proneness (“How often do you feel nostalgia?”) and nostalgia’s importance
(“How significant is it for you to feel nostalgia?”) to an individual. Items range from 1 (“Not at
all”) to 7 (“Very Much”). A total score was calculated by averaging all seven items (with one
backward-scored item).

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Stimulus Selection
The music stimulus for this study was the full-length version of “A Day in the Life” (Remastered
2010) by The Beatles (The Beatles, 1967). We chose this song because we expected that it would
be familiar to most participants and nostalgic to at least some participants. To select this song,
we conducted the following steps:
1. In a previous behavioral study with 782 participants from across the United States
(Hennessy et al., in press), participants were asked to self-report songs that made them
feel nostalgic. The Beatles were the top-reported artists in the older adult age cohort
(those 60 years and older). Then, we collected the self-selected songs by The Beatles
from this group of participants and retrieved Popularity metrics using Spotify’s Music
Information Retrieval (MIR) API (Lamere, n.d.). This metric scored each song on a scale
of 0 to 100, based on how much it is streamed from the Spotify library. The most popular
song from this list was “A Day in the Life” (Spotify URL:
https://open.spotify.com/embed/track/0hKRSZhUGEhKU6aNSPBACZ), with a
Popularity of 63.
2. We confirmed this choice using information about the present study’s age cohort. First,
we calculated the median year at which participants in the study were age 14 (1968). This
age was chosen as it has previously been reported as the peak of the “reminiscence
bump” from music (Jakubowski et al., 2020; Krumhansl & Zupnick, 2013). Thus, songs
from this age period likely evoke strong emotional memories like nostalgia. This year,
1968, is closely aligned with the release year of “A Day in the Life” (1967). Additionally,
we found that Sgt Pepper’s Lonely Hearts Club Band, on which “A Day in the Life” was

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released, remained in the Top 200 billboard albums for the entirety of 1968 (Cabison,
2013).
From this information, we concluded that “A Day in the Life” would likely be a familiar and
perhaps nostalgic song to most participants aged 60 and older. All MIR features were retrieved
from “A Day in the Life” using Spotify’s API and are reported in Table 2.
Table 2.
MIR features of musical stimulus played during scan.
Chapter 4 Table 2
Track Name "A Day in the Life"
Artist The Beatles
Release Date 6/1/1967
Album Sgt. Pepper's Lonely Hearts Club
Band
energy 0.46
valence 0.18
popularity 63
danceability 0.36
loudness -14.16
key E
mode Minor
tempo 163 bpm
time-signature 4-Apr

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liveness 0.92
instrumentalness 0.0001
speechiness 0.07
acousticness 0.29
Note. These features were retrieved using Spotify’s web-based API, which derives features from
the majority of time points in a song. We note that “A Day in the Life” contains multiple tempo
and key changes, and these metrics reflect only the average values across the song. Acousticness,
danceability, instrumentalness, liveness, and speechiness are on a scale of 0 to 1, in which a score
closer to one denotes more intensity of that feature. Valence and energy are also on a scale of 0 to
1; however, 0.5 indicates the “neutral” midpoint, 1 is high or positive, and 0 is low or negative.
Popularity is on a scale of 0 to 100, where higher values indicate more streams on Spotify.
fMRI task
During the one-hour scan, participants completed two functional tasks. During the first,
participants listened to multiple nostalgic and non-nostalgic pieces of music personalized to each
participant. The results from this task are reported in Hennessy et al. (in preparation). In the
second task, participants listened to The Beatles' full-length version of “A Day in the Life” (The
Beatles, 1967). While listening to the song, participants were told to keep their eyes closed and
remain still. A five-second buffer was included before the beginning of the song to account for
scanner drift. At the end of the song, participants were asked to indicate, on a scale of 1 to 9,
“How nostalgic did you feel while listening to that song?” where 1 indicated “Not nostalgic at
all” and 9 indicated “Extremely nostalgic.” Prior, participants were defined nostalgia as “a
sentimental longing for the past.” Participants were also asked, “Were you familiar with the song

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before hearing it today?” with options “Yes” or “No.” Only participants who responded “Yes” to
this question were included in subsequent analyses to control for the effects of familiarity (this
removed one participant). Music was played through fMRI-safe active noise-canceling
headphones that actively attenuated the noise of the functional scan sequence (OptoAcoustics
OptoActive) to allow participants to hear the music over the sound of the scanner.
Neuroimaging parameters
A 3T Siemens MAGNETON Prisma System with a 32-channel head coil, located at the Dana
and David Neuroimaging Institute at the University of Southern California, was used for this
study. We obtained high-resolution T1-weighted structural MRI images (1mm x 1mm x 1mm
resolution, over a 256 mm x 256 mm x 256 mm FOV, TR = 2300 ms, TE = 2.05 ms; flip angle =
8°) using a 3D magnetization-prepared rapid acquisition gradient (MPRAGE) sequence.
Diffusion-weighted images were also obtained during the scan session and will be reported in a
future manuscript.
Functional images were obtained using a gradient-echo, echo-planar T2*-weighted
multiband pulse sequence with a multiband factor of M = 8 (TR = 1000ms, TE = 35 ms, flip
angle = 52°, 68 x 68 mm in-plane resolution). 48 slices were obtained with 3 mm x 3 mm x 3
mm voxel resolution. For the listening task, 315 functional volumes were obtained. The gradientecho field map was obtained to correct for field-inhomogeneity in analysis (TR = 1000ms, TE1 =
5.19 ms, TE2 = 7.65 ms, flip angle = 60°, 68 x 68 mm in-plane resolution).
Analysis
fMRI preprocessing
Pre-processing and analyses of functional images were performed using FSL version 6.0
(Jenkinson et al., 2012). Skull stripping was performed using FSL’s BET brain extraction tool.

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The gradient-echo field map was used to correct inhomogeneity in the magnetic field using
FSL’s FUGUE unwarping tool (anterior-posterior, 10% signal loss threshold). Motion correction
was performed using FSL’s MCFLIRT. Slice timing correction was performed with Fourierspace time series phase shifting, and spatial smoothing (5.0 mm FWHM Gaussian kernel). Highpass temporal filtering with a Gaussian weighted least-squares line (sigma = 100 s) was
performed. Functional images were initially registered to their T1 image using FSL’s FLIRT and
then further nonlinearly registered to standard space (MNI 152 space) with a 12-degree of
freedom affine transformation using FSL’s FNIRT. Motion artifacts were removed using ICAAROMA (Pruim et al., 2015). Images were transferred from native to standard space (MNI 152).
Finally, files were trimmed to cut out the 5-second pre-listening rest period and the post-listening
question period so that only the period of music listening was included for each participant.
Whole Brain Intersubject Correlation Analysis
After pre-processing, an intersubject correlation (ISC) correlation was calculated to assess the
extent to which the time-varying stimulus aligned with neural responses across participants.
Analyses were conducted using Python version 3.12 with modules from the Brain Imaging
Analysis Kit (brainIAK), http://brainiak.org (Kumar et al., 2020, 2021) and nilearn (Nilearn
contributors, n.d.). For each participant at each voxel in the brain, the time course from that
voxel was correlated using Pearson’s correlation coefficient with the average of all other
participants following a leave-one-out approach, using brainIAK’s isc function (Kumar et al.,
2020, 2021). The resulting matrix was a 27 by 228,483 matrix of correlation values,
corresponding to N = 27 participants and N = 228,483 voxels. Then, we performed
nonparametric statistical testing using permutation tests (Pajula et al., 2012) to evaluate whether
correlations at each voxel significantly differed from zero. We computed a null distribution of

230
values at each voxel in the brain using brainIAK’s permutation_isc function with 10,000
permutations. P-values of the correlation statistic for each value were then calculated based on
the simulated null distribution. We then corrected for multiple comparisons using the False
Discovery Rate (FDR; Benjamini & Hochberg, 1995), q < 0.05, resulting in a final image
containing only voxels in which the ISC value was significantly different than zero across the
brain. Results were visualized using FreeSurfer’s FreeView.
Behavioral correlates. We next assessed whether state or trait nostalgia altered ISC
during the music-listening task. State-level nostalgia ratings (“How nostalgic did that song make
you feel”) were mean-centered across participants. We computed correlations across participants
at each voxel in the brain between nostalgia ratings and r statistics from the whole-brain ISC
map created in the previous step. Non-parametric statistical testing was performed using
permutation tests. We first computed a null distribution of correlation values at one voxel in the
brain, using 100,000 permutations. From the null distribution, p values for the observed
correlation statistic for each voxel were calculated, and FDR correction was applied (q < 0.05).
The result was a final image containing only voxels with a significant correlation between
nostalgia ratings and ISC across participants. Separately, the same steps were repeated for meancentered trait nostalgia (as measured with SNS).
ROI-based Intersubject Correlation Analysis
To further investigate inter-subject synchrony in neural regions hypothesized to be
involved in nostalgic listening, we conducted region-of-interest ISC. These ROIs were selected
due to their involvement in reward (ventral tegmental area, ventral striatum), autobiographical
memory (medial temporal lobe), self-referential processing (medial prefrontal cortex,
ventromedial prefrontal cortex, postero-medial cortex), and auditory processing (Heschl’s gyrus).

231
We create 8-voxel spheres with center coordinates at peak clusters reported in published metaanalyses on autobiographical memory, reward, and auditory processing (see Table 3). Due to
overlapping spheres on cortical midline structures (VTA, mPFC, vmPFC, PMC), adjusted center
coordinates were used such that the absolute value of the X coordinate had a minimum value of
8.
Table 3.
Regions of interest, center coordinates, and meta-analyses from which ROIs were defined.
Chapter 4 Table 3
Functional Network ROI Center coordinates Derived from
Default mode mPFC L: (-8, 54, 14)
R: (8, 54, 14)
Kim, 2012
vmPFC L: (-8, 42, -16)
R: (8, 42, -16)
Morelli et al., 2015
PMC L: (-8, -59, 29)
R: (8, -59, 29)
Kim, 2012
Autobiographical Memory MTL L: (-24, -33, -24)
R: (21, -29, -19)
Kim, 2012
Reward VTA L: (-8, -16, -14)
R: (8, -14, -4)
Diekhof et al., 2012
VS L: (-10, 10, 2)
R: (12, 14, -4)
Diekhof et al., 2012
Auditory HG L: (-32, -26, 8) Chan & Han, 2022

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R: (54, -10, 6)
Note. mPFC = medial prefrontal cortex, vmPFC = ventromedial prefrontal cortex, PMC =
posteromedial cortex, MTL = medial temporal lobe, VTA = ventral tegmental area, VS = ventral
striatum, HG = Heschl’s gyrus.
For each participant, data were masked and the time-course was extracted and averaged
across all voxels within each ROI (left and right separately). The average time course for each
participant was correlated with the average of the rest of the sample in a leave-one-out analysis,
resulting in a correlation matrix of 27 participants by 14 ROIs. Non-parametric tests were
conducted with permutation testing to assess if ISC values were significantly greater than zero
for each ROI, with 10,000 permutations. P values were calculated from the simulated null
distribution, and FDR correction was applied (number of comparisons = 14) thresholding
significance at an alpha level of 0.05.
Behavioral correlates. Within each ROI, we assessed whether state or trait nostalgia
altered ISC. We correlated r statistics with behavioral ratings of trait and state nostalgia
separately for each region-of-interest, and applied FDR correction (number of comparisons =
14). These analyses used R version 4.3.2 (R Core Team, 2023) and R Studio. Results were
visualized using the ggplot package in R (Wickham, 2016).
Results
Intersubject correlation during music listening
Whole Brain Results

233
For peak coordinates for each region, see Table 4. We observed significant intersubject
correlation across the entire piece of music in large clusters in the bilateral auditory cortex,
including Heschl’s gyrus, planum temporale, and anterior and posterior superior temporal gyrus
(see Figure 1). Bilaterally, these clusters extended superiorly to the central and parietal
operculum and supramarginal gyrus, and inferiorly to the posterior, anterior, and
temporooccipital middle temporal gyrus. Posteriorly, these clusters extended to the bilateral
angular gyri, and anteriorly to the temporal poles. We additionally detected significant clusters in
the bilateral superior parietal lobule, precentral gyrus, inferior frontal gyrus, orbitofrontal cortex,
ventromedial prefrontal cortex, and insula. Along the midline, a large significant cluster was
observed in the bilateral supplementary motor cortices, with smaller clusters in the bilateral
precuneus, posterior and anterior cingulate cortex. Clusters were observed in the bilateral
occipital lobe at the occipital pole and lateral occipital cortex. Subcortically, small clusters were
observed in the left amygdala, bilateral parahippocampal gyri, bilateral hippocampi, bilateral
thalamus, and bilateral caudate. In the cerebellum, significant clusters were observed in bilateral
VI, vermis IX, crus I, and crus II, VIIIa.
Table 4.
Coordinates of significant clusters for whole brain results.
Chapter 4 Table 4
Area r
value
x y z
Frontal L ACC 0.06 -7 36 16
L IFG 0.2 -47 13 25
L insula 0.12 -36 22 -2
L OFC 0.07 -36 34 -
18
L precentral gyrus 0.42 -51 -3 53

234
L SMA 0.28
-
3
2 62
L VMPFC 0.12
-
6 62
2
R ACC 0.08
2 41
-
4
R IFG 0.24 56 23 26
R insula 0.09 40 20
-
4
R OFC 0.06 33 28
-
20
R precentral gyrus 0.4 56
-
1 48
R SMA 0.24
3
0 63
R VMPFC 0.07
6 50
-
2
Temporal L angular gyrus 0.25
-63
-56 13
L central
operculum
0.19
-52
-14 15
L HG 0.57
-52
-21
7
L MTG 0.23
-52
-55 10
L parietal
operculum
0.43
-43
-38 18
L planum
temporale
0.59
-61
-22
8
L SMG 0.44
-60
-42 17
L STG 0.67
-65
-26
2
L temporal pole 0.28
-56 11
-
11
R angular gyrus 0.29 62
-48 14
R central
operculum
0.16 50
-17 15
R HG 0.43 48
-21
9
R MTG 0.27 57
-52 13
R parietal
operculum
16 44
-31 20
R planum
temporale
0.5 62
-22 10
R SMG 0.46 56
-40 12
R STG 0.62 65
-18
0
R temporal pole 0.32 57 10
-
11
Parietal L PCC 0.12
-
4
-30 26
L precuneus 0.16
-
2
-73 38
L SPL 0.11
-30
-46 50
R PCC 0.12
2
-32 26
R precuneus 0.16
5
-73 40

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R SPL 0.13 28 -50 65
Occipital L lateral occipital
cortex
0.14 -21 -85 45
L occipital pole 0.12 -22 -100 -7
R lateral occipital
cortex
0.13 39 -81 2
R occipital pole 0.12 22 -90 7
Subcortical L amygdala 0.06 -27 -2 -
16
L caudate 0.07 -11 5 11
L hippocampus 0.04 -28 -12 -
24
L PHG 0.07 -33 -12 -
27
L thalamus 0.1 -10 -14 12
R caudate 0.07 11 9 9
R hippocampus 0.03 23 -16 -
20
R PHG 0.05 29 -10 -
34
R thalamus 0.1 -6 -16 10
Cerebellar L crus I 0.1 -8 -78 -
29
L crus II 0.18 -15 -76 -
43
L VI 0.19 -26 -65 -
21
L VIIIa 0.2 -29 -62 -
51
R crus I 0.1 46 -63 -
40
R crus II 0.14 18 -75 -
43
R VI 0.18 29 -61 -
21
R VIIIa 0.22 25 -63 -
53
Note. Coordinates are in MNI space. ACC = anterior cingulate cortex, IFG = inferior frontal
gyrus, OFC = orbitalfrontal cortex, SMA = supplementary motor area, VMPFC = ventromedial
prefrontal cortex, HG = Heschl’s gyrus, MTG = middle temporal gyrus, SMG = supramarginal

236
gyrus, STG = superior temporal gyrus, PCC = posterior cingulate cortex, SPL = superior parietal
lobule, PHG = parahippocampal gyrus.
Figure 1.
Significant whole-brain ISC map during music listening.
Chapter 4 Figure 1
Note. Color bar indicates Pearson’s r statistic (correlation across participants). Maps are
corrected for multiple comparisons using FDR (q > 0.05).
ROI Results
Intersubject correlation in a-priori-defined regions of interest revealed correlations significantly
different from zero in the left (r = 0.20, SE = 0.02, pcorrected < 0.01) and right Heschl’s Gyrus (r =
0.43, SE = 0.03, pcorrected < 0.01), and right ventral tegmental area (r = 0.08, SE = 0.03, pcorrected <

237
0.05). All other ROIs were not significant after correcting for multiple comparisons (ps > 0.05).
Mean correlation values, standard errors, and corrected p values for each ROI are presented in
Table 5 and visualized in Figure 2.
Table 5.
Intersubject correlation values within regions of interest.
Chapter 4 Table 5
ROI Correlation (r) Standard Error pcorrected
L HG 0.20 0.02 0.001**
L MPFC 0.04 0.02 0.32
L MTL 0.03 0.02 0.43
L PMC 0.06 0.03 0.14
L vMPFC 0.03 0.03 0.26
L VS 0.02 0.02 0.35
L VTA 0.05 0.02 0.19
R HG 0.43 0.03 0.001**
R MPFC 0.05 0.02 0.40
R MTL 0.04 0.03 0.35
R PMC 0.07 0.03 0.09
R vMPFC 0.02 0.02 0.43
R VS 0.05 0.02 0.05
R VTA 0.08 0.03 0.03*

238
Note. mPFC = medial prefrontal cortex, vmPFC = ventromedial prefrontal cortex, PMC =
posteromedial cortex, MTL = medial temporal lobe, VTA = ventral tegmental area, VS = ventral
striatum, HG = Heschl’s gyrus. Error bars represent standard error. Stars indicate statistical
significance (* pcorrected < 0.05, ** pcorrected < 0.001, *** pcorrected < 0.001). P values are corrected
using FDR.
Figure 2.
Mean correlation values across participants in regions of interest.
Chapter 4 Figure 2
Note. HG = Heschl’s gyrus , MTL = medial temporal lobe, VS = ventral striatum, VTA = ventral
tegmental area, PMC = posteromedial cortex, mPFC = medial prefrontal cortex, vmPFC =
ventromedial prefrontal cortex. Error bars represent standard error. Stars indicate statistical
significance (* pcorrected < 0.05, **pcorrected < 0.001, *** pcorrected < 0.001). P values are corrected
using FDR.

239
Effect of state nostalgia on ISC during music listening
Distributions of State and Trait Nostalgia across the sample are reported in Figure 3. In the whole
brain analysis, neither state nor trait nostalgia ratings significantly predicted ISC during music
listening in any voxel of the brain. In ROI analyses, no significant correlations were detected
between state (Figure 4) or trait (Figure 5) nostalgia ratings and ISC within any region of interest
(ps > 0.05).
Figure 3.
Distributions of state and trait nostalgia.
Chapter 4 Figure 3

240
Figure 4.
Correlations between ROI-based ISCs and State Nostalgia ratings.
Chapter 4 Figure 4
Note. HG = Heschl’s gyrus, MTL = medial temporal lobe, VS = ventral striatum, VTA = ventral
tegmental area, PMC = posteromedial cortex, mPFC = medial prefrontal cortex, vmPFC =
ventromedial prefrontal cortex. Error bars represent standard error. Error bands indicate standard
errors. No correlations were statistically significant (pcorrected > 0.05). P values are corrected using
FDR.

241
Figure 5.
Correlations between ROI-based ISCs and Trait Nostalgia ratings.
Chapter 4 Figure 5
Note. HG = Heschl’s gyrus , MTL = medial temporal lobe, VS = ventral striatum, VTA = ventral
tegmental area, PMC = posteromedial cortex, mPFC = medial prefrontal cortex, vmPFC =

242
ventromedial prefrontal cortex. Error bands indicate standard errors. No correlations were
statistically significant (pcorrected > 0.05). P values are corrected using FDR.
Discussion
In a sample of twenty-seven older adults, this study used a full-length version, “A Day in the
Life” by The Beatles, to examine the neural basis of nostalgia in an ecologically-valid manner.
We show that (1) the most robustly synchronized regions of activity were auditory and motor
areas, (2) smaller clusters of synchronized activity were observed in the default mode network,
reward regions, and medial temporal lobe, and (3) neither state nor trait nostalgia modulated
intersubject synchronization in any region.
Our whole-brain and ROI-based results align with existing literature on music listening,
music-evoked emotions, and nostalgia. Our largest clusters of synchrony across participants were
in the bilateral auditory cortices, inferior frontal gyri, and motor regions (precentral gyri, SPL,
supplementary motor cortices). These regions are implicated in auditory and music processing
across univariate neuroimaging studies (Janata, 2015). We replicate this finding in an ecologicalvalid context, corroborating previous ISC studies (Abrams et al., 2013; Alluri et al., 2013; Regev
et al., 2021; Sachs et al., 2020; Trost et al., 2015).
Additionally, we revealed significant inter-subject neural synchrony during nostalgic
music listening in regions implicated in self-referential processing (posterior cingulate,
precuneus, medial prefrontal cortices, angular gyri), autobiographical memory (parahippocampal
gyrus, hippocampus, precuneus, temporal poles), emotion regulation (anterior cingulate), and
reward (caudate, thalamus). These clusters were smaller than the activation observed in primary
auditory regions but remained significant after multiple comparison corrections. These regions of
synchronization are in line with previous univariate studies on music-evoked nostalgia (Barrett &

243
Janata, 2016; Hennessy et al., in prep; Trost et al., 2012) and music-evoked autobiographical
memory (Janata, 2009; Kubit & Janata, 2018) . They also align with ISC-based studies
investigating episodic memory retrieval, not specifically evoked by music (Sheng et al., 2023).
We found this relevant given nostalgia’s highly autobiographical nature (Barrett et al., 2010;
Garrido & Davidson, 2019). Together, these additional regions of synchrony provide preliminary
evidence for the neural basis of nostalgia in a naturalistic listening environment.
Interestingly, ours is not the first study to utilize music by The Beatles in a study of
neural synchronization with fMRI. Alluri and colleagues (2013) used excerpts from The Beatles’
album Abbey Road (1969) and compared these selections to those from other genres that did not
contain lyrics (“Medley”). In addition to previously reported auditory and motor regions, they
found synchronization during only the Abbey Road selections in the posterior cingulate, fusiform
gyrus, cerebellum, hippocampus, and parahippocampal gyrus. These findings were attributable to
the presence of lyrics within the music; however, it is entirely possible that these selections were
more memory-evoking or nostalgic than the other selections in their study. Participants in their
study were young adults who were, based on the published year of the study and the mean
participant age, born in the late 1980s. This places participants firmly in the Millenial generation
(year of birth between 1981 and 1996), who are most often parented by individuals in the Baby
Boomer generation (year of birth between 1946 and 1964) (Dimock, 2019). There is considerable
evidence for cross-generational sharing of nostalgic songs (‘cascading reminiscence bumps’)
(Jakubowski et al., 2020; Krumhansl & Zupnick, 2013; Schubert, 2016), in which parents’ most
nostalgic songs eventually become part of children’s nostalgic songs. Unlike the songs in the
“Medeley” collection (i.e., music by Vivaldi, The Shadows), Abbey Road was a number one topranked album during the late sixties (Caulfield, 2019), situating it directly in the peak likelihood

244
of eliciting nostalgia (Jakubowski et al., 2020) for individuals from the Baby Boomer generation.
Thus, while Alluri and colleagues (2013) attributed synchrony in these additional memory and
self-related regions to lyrical processing, it may have reflected nostalgia or autobiographical
salience instead. If true, this would align with findings in the present study, in which similar selfrelated and autobiographical memory-related regions were observed to synchronize across
participants.
Contrary to our hypothesis, state nostalgia did not modulate ISC in our whole-brain or
ROI analyses. This could be interpreted in several ways. First, this could indicate that stimulusdriven neural patterns observed during nostalgic listening are highly consistent across
individuals. That is, once a threshold of nostalgia is reached, there is little variation in these
responses attributable to the intensity of nostalgic feeling. Alternatively, it could indicate that the
identified regions of synchronization are not related to nostalgia at all and, instead, merely reflect
synchronization that may occur during any shared music listening experience. However, the
overall intensity of nostalgia felt during listening was high, indicating that we successfully
induced a nostalgic listening experience. Additionally, we observed clusters of activity in wholebrain and ROI analyses implicated in nostalgic processing, above and beyond music listening in
general. Specifically, we found activation in the default mode network (precuneus, posterior
cingulate, medial prefrontal cortex, temporal pole, angular gyrus). Still, given this study's lack of
control condition and the absence of participants who rated the song as “not at all nostalgic,” we
cannot rule out this possibility.
Similarly, we observed no effect of trait nostalgia on synchrony during listening across
participants. That is, older adults who were more nostalgic in their daily lives did not have
greater or lesser synchrony with the group during the nostalgic listening experience. Previous

245
work has observed that trait nostalgia modifies the neural response to nostalgia in younger
adults, such that younger adults who were less nostalgic showed greater reward activity scaled
with felt nostalgia during music listening (Barrett & Janata, 2016). However, in a previous report
from the same sample presented here, our group found that trait nostalgia impacted neural
response to music in younger but not older adults. This suggests an age-related stabilization of
the neural nostalgic response regarding person-level factors. Our findings present preliminary
evidence that the time-locked neural activity during nostalgic listening is robust to trait-level
nostalgia in older adults. This also underscores the importance of including participants across
the age spectrum in neuroimaging emotion research.
Limitations and Future Directions
A central limitation of the present work is the lack of a non-nostalgic control song and the
accompanying small range of state nostalgia ratings. We did not scan participants while listening
to a full-length, non-nostalgic piece of music. Thus, we cannot infer that synchronized activity in
our whole-brain analysis was due to the nostalgic quality of the piece or simply to the experience
of listening to any piece of music. We intended to remedy this concern by quantifying whether
the degree of felt nostalgia predicted synchrony between participants. However, we observed a
relatively small range of state nostalgia values in response to our stimulus, limiting the possible
range for this analysis. Still, we believe the observed results are informative within their scope.
Future work could examine differences between nostalgic and non-nostalgic stimuli or
selectively recruit participants based on nostalgic response to the stimulus until a more normally
distributed range of state nostalgia values is achieved.
Secondly, future work on nostalgia might consider including continuous ratings of
emotion during music listening. This method can illuminate finer-grained emotional state

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differences as they unfold over time. Previous work utilizing dynamic ISC methods with music
listening, such as sliding window ISC, has successfully examined moment-to-moment changes in
synchrony as they relate to changes in feeling (Sachs et al., 2020). Before this addition, however,
behavioral work is needed to determine whether feelings of nostalgia or even ratings of positivity
and negativity vary across the duration of a nostalgic musical piece at all.
Lastly, more work is needed to explore the dynamics of nostalgic listening across the age
spectrum. Future work could consider including older and younger adults, with each group
assigned a cohort-appropriate nostalgic song. This would shed light on whether the findings
observed in the present study are stable across the lifespan.
Conclusions
We observed neural synchrony across older adults during a shared nostalgic music-listening
experience, unmodulated by trait or state nostalgia. This synchrony was most robustly
represented in auditory and motor regions and secondarily in default mode, medial temporal
lobe, and reward regions. By examining these processes in an ecologically valid listening
environment, this work adds to our nascent understanding of the neural underpinnings of
nostalgia and opens avenues for future work.

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Chapter 5: Conclusion
The four previous chapters have explored the concept of music-evoked nostalgia across the
lifespan, outlining its psychological and neural basis. In this work, we asked and answered the
following questions: 1) What is the affective signature of music-evoked nostalgia, and how do
individual differences influence this experience? 2) What is the content of music-evoked
nostalgic experiences? 3) What are the neural correlates of music-evoked nostalgia in healthy
younger and older adults in the context of a highly personalized and shared nostalgic experience?
These studies present significant progress toward a more complete understanding of musicevoked nostalgia and illuminate opportunities for future research.
In Chapter 1, I characterized the affective profile of self-selected music-evoked nostalgia
compared to non-nostalgic, musically-matched, familiar music (Hennessy et al., 2024). In a
behavioral study with 582 participants, I probed feelings of valence and arousal after listening to
personalized music and related these outcomes to personality. I observed that music-evoked
nostalgia is a primarily positive emotion, with some evidence for mixed feelings. Personality
predicts how an individual would feel after listening to nostalgic music, not non-nostalgic music.
While some found nostalgic music to make them feel more aroused and positive (those high in
Trait Nostalgia, Care, and Anger), others felt more negative while listening (those high in
Sadness). Lastly, I expanded upon previous accounts of a highly nostalgic person; trait-level
Trait Nostalgia was associated with Care, Play, Agreeableness, Extraversion, and Neuroticism.
This chapter is now published in Emotion (Hennessy et al., 2024).
In Chapter 2, I described the content of nostalgic experiences as evoked by self-selected
music in an analysis of 2,100 texts across 744 participants. Participants reported brief
descriptions of the content of a nostalgic experience after listening to self-selected nostalgic

248
music. I observed that the release date of nostalgic songs peaked when participants were between
the ages of 10 and 30 (the “reminiscence bump”). For songs released before a participant’s birth
date, I found that the release date fell neatly within the participant’s parent’s reminiscence bump
(“cascading reminiscence bump”). The content of nostalgic experiences reflected that of
nostalgia's social and psychological functions; texts were highly self-relevant and social,
containing more positive than negative emotion words. Similar to the study presented in Chapter
1, I observed that the presence of negative emotion words was predicted by Sadness.
In Chapter 3, I examined the neural correlates of music-evoked nostalgia in younger and
older adults. Self-selected nostalgic music, more than familiar and musically-matched nonnostalgic music, showed greater activation across age groups in the default mode network
(DMN), medial temporal lobe, and reward regions. Nostalgic listening additionally involved the
integration of auditory with posterior DMN regions and posterior DMN with the insula, whereas
non-nostalgic listening involved connectivity from auditory and DMN with feature-processing
regions. Assessment between age groups revealed that neural responses during nostalgic listening
were generally greater in older, compared to younger adults, in a distributed network of
nostalgia-related regions. Lastly, while the neural response to nostalgic music in younger adults
was modified only by trait-level factors of nostalgia proneness and cognitive ability, the response
in older adults was modified only by affective response tendencies. This study was the first to
investigate the neural basis of music-evoked nostalgia in younger and older adults and the first
neural investigation of nostalgia to utilize self-selected stimuli.
In Chapter 4, I examined neural synchrony among a group of older adults as they listened
to the full-length version of a nostalgic song: “A Day in the Life” (The Beatles, 1967). I observed
significant stimulus-driven synchrony in regions observed in Chapter 3 to be implicated in

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nostalgia (posterior cingulate, precuneus, medial prefrontal cortex, caudate), as well as those
typically observed in music listening (auditory cortex, supplementary motor area, inferior frontal
gyrus). Neither state nor trait nostalgia modulated intersubject synchrony during music listening.
This study was the first to use intersubject correlation to investigate nostalgia.
Together, these studies shed light on music-evoked nostalgia's behavioral and neural
basis. I demonstrate that adult nostalgic experiences are primarily positive, triggered by music
released during adolescence, and that the nature of the “past” to which individuals are mentally
transported is one of social connectedness and self-reflection. These experiences are supported
by a distributed network of brain regions including, but not limited to, the default mode network,
medial temporal lobe, reward regions, and auditory cortex. These regions support the listener
engage in self-referential and autobiographical thought in tandem with music-derived pleasure.
Across studies, I show that individual differences impact the felt experience of nostalgia and the
representation of this experience in the brain. Notably, I show that these relationships change
with age, which may reflect adaptive shifts in motivations and emotion processing. These
exciting advances raise several points that are worth discussing in more depth below.
The Promise of Self-Selection
One of the critical methodological contributions of the work presented in this dissertation is the
development of a music recommendation tool. This tool, now titled “SoundsLikeThis,” enabled
us to personalize musical stimuli for each participant. The greatest merit of this was that
participants were allowed to choose their nostalgic songs, circumventing previous limitations in
the literature related to the heterogeneity of human musical preference. While many of the songs
selected by participants in these studies were indeed from the Billboard Top 100 from the peak of
a participant’s “reminiscence bump,” a significant portion of songs were not. Anecdotally, these

250
were some of the most impactful moments I had with participants (e.g., watching them listen to a
traditional Hungarian folksong sung to them 50 years ago by their mother at bedtime). The
diverse musical experiences that reflect an individual’s culture, geographical location, listening
preferences, and personality cannot be overstated. Without allowing these unique and
personalized experiences into the laboratory, I do not believe we can fully begin to understand
music-evoked emotions or autobiographical memories.
In these studies, we showed that our music recommendation tool was able to identify
musically matched, familiar, non-nostalgic music for each participant’s self-selected nostalgic
songs. This allowed for systematic control of the variables of interest while making room for
personalization. This has two major implications moving forward.
First, I hope that other researchers will use this tool, which is now freely available at
SoundsLikeThis.com (see Figure 1). Online, a researcher or interested party can input a song or
spreadsheet of songs and choose the relevant musical parameters they would like to control (e.g.,
valence, release date, acousticness). The machine then outputs a list of N songs matched to the
input song, which can be further evaluated with psychometric questionnaires delivered to
participants.

251
Figure 1.
SoundsLikeThis online music-matching tool.
Secondly, an unintended consequence of using this tool across studies was the
overwhelmingly positive feedback we received from participants about the enjoyment of the
musically matched songs overall, regardless of whether they could be used as eventual stimuli.
By matching for specific features on top of the base Spotify algorithm, we were able to
inadvertently create enjoyable playlists for participants based on their seed nostalgic songs,
comprised of 1) unfamiliar songs that they enjoyed due to their preferential acoustic features, 2)
familiar, enjoyable, non-nostalgic songs, 3) “surprise” nostalgic songs (for example, songs that
the participant had forgotten about that involuntarily evoked nostalgia). While general playlist
Chapter 5 Figure 1

252
generation was not the focus of this dissertation, it is possible that this could be a valuable tool
for future research with individuals with Alzheimer’s Disease and Related Dementias. Stimulus
selection for music-based trials typically involves the expertise of a board-certified Music
Therapist, which can be costly and time-consuming. If playlist generation using deliberately
manipulated Spotify features proves to be effective in these settings, this may help to ease
researcher and caregiver burden while providing an enjoyable experience for the participant.
Nostalgia as a path toward more vivid remembering
A topic of interest outside the general scope of this dissertation is the relationship between
music-evoked nostalgia and autobiographical memory vividness. Previous work has
demonstrated that music-evoked autobiographical memories are more vivid (i.e., contain more
event-related rather than general details) than those evoked by photos (Belfi et al., 2016),
television (Jakubowski et al.., 2021) and food (Jakubowski et al., 2023). However, no work to
our knowledge has compared the vividness of memories evoked by nostalgic music with those of
non-nostalgic music. Given the phenomenological and neural differences between nostalgic and
non-nostalgic music experiences presented in Chapters 1 and 3, respectively, one would expect
that nostalgic music-evoked memories might be more vivid. If so, it may be concluded that
nostalgia as a music-evoked emotion has properties that help mentally transport individuals to a
particular time more effectively than music alone. If not, it may suggest that while nostalgic
music evokes more vivid feelings and associated neural activity, this feeling may not confer
additional memory retrieval benefits. Instead, the intensity of the feeling may even hinder the
ability to vocalize specific event-related details to an experimenter, even if the event is more
strongly mentalized.

253
A study investigating this question is underway, with results expected in Summer 2024.
In this preregistered study, we enrolled 150 Black, Indigenous, and Persons of Color (BIPOC)
older adults (aged 50-80) in a 13-week study on nostalgic music-evoked autobiographical
memories. Each week, participants listened to either a nostalgic or non-nostalgic piece of music
and recalled an autobiographical memory, involuntarily or prompted, with a research assistant
via Zoom. Two thousand memories were coded using the Autobiographical Interview (Levine et
al., 2002) coding procedures by a dedicated team of Brain and Music Project undergraduate
Research Assistants. As of May 2024, all memories will be coded, and data analysis will begin,
assessing whether nostalgic music confers additional benefits to memory vividness than familiar
music. While relevant to this dissertation, this work was not included in the present compilation
due to the funding timeline (which began after proposing the dissertation during the Qualifying
Exam).
Applications for Alzheimer’s Disease and Related Dementias
Finally, a central motivation for the research presented in this dissertation was its
application to individuals with Alzheimer’s Disease (AD). Anecdotal evidence for preserving
music-evoked autobiographical memories in dementia suggests that musical pieces may
temporarily “unlock” an individual with otherwise apparent memory impairment (RossatoBennett, 2014). Music listening programs improve symptoms (Lineweaver et al., 2021) and
reduce antipsychotic medication use in AD (Thomas et al., 2017). Music selected by researchers
has been shown to improve autobiographical memory (AM) recall in adults with dementia ((El
Haj et al., 2015; Haj et al., 2012; Foster & Valentine, 2001; Irish et al., 2006; Meilán García et
al., 2012). Several small pilot studies have observed that encoding of longer-known or preferred
music activated the ACC and pre-SMA (Jacobsen et al., 2015; King et al., 2019; Thaut et al.,

254
2020), as well as subcortical (insula, putamen, thalamus), temporal (STG, MTG), medial
temporal (hippocampus, parahippocampal gyrus, amygdala), and default mode regions
(precuneus, mPFC) (Thaut et al., 2020). Comparing individuals with behavioral-variant
frontotemporal dementia (bv-FTD) with individuals with Alzheimer’s Disease, Baird et al.
(2020) observed that while Alzheimer’s Disease patients demonstrated typical frequency and
specificity of music-evoked autobiographical memories compared to healthy older adults,
individuals with bv-FTD did not, suggesting that frontal-temporal regions play a critical role in
music-evoked autobiographical memories. In support of this, Belfi et al. (2018) observed that
individuals with MPFC damage had impaired memories of music, while photo-evoked memories
remained intact. Overall, these studies illustrate that long-known, familiar, and preferred music
activates many of the same regions in patients with Alzheimer’s Disease as those observed in
healthy younger and older adults, as observed in Chapters 3 and 4, and highlight the particular
importance of the medial prefrontal cortex in music-evoked autobiographical memories.
What is missing from this literature is a full investigation of nostalgic music in
individuals with AD. Given the prominent role of the medial prefrontal cortex in nostalgic
processing observed in Chapters 3 and 4, nostalgic music may likely especially evoke
autobiographical memories and autonoetic experiences in individuals with AD, as supported by
this and related neural regions. The reorganization of the default mode network with age and in
the presence of neurodegenerative diseases (Andrews-Hanna et al., 2019) puts greater reliance on
the dorsomedial prefrontal sub-system of the DMN. Chapter 2's findings suggest that both
healthy younger and older adults rely on this subsystem (including DMPC, temporal pole, and
angular gyrus), suggesting a promising ease in preserving music-evoked nostalgia in AD. As a
clarifying note, I nor anyone in this field that I am aware of, do not endorse the view that music

255
will cure the underlying pathology associated with Alzheimer’s Disease. Instead, nostalgic music
may provide short-lived moments of respite, during which a patient may experience a temporary
return to self. These experiences, over time, may ease the psychological toll of Alzheimer’s
Disease on the patient and their loved ones.
Final Remarks
To conclude, nostalgia has traveled a long journey since its inception in Johannes Hofer's 17thcentury mind. Once conceptualized as a disease of the “middle brain” triggered by the sound of
Swiss cowbells, we now understand nostalgia as a largely positive mixed feeling accompanied
by a distributed network of neural activity that is preserved across the lifespan. Future work on
this topic should examine music-evoked nostalgic processing as it relates to autobiographical
memory vividness and its neural correlates in patients with Alzheimer’s Disease and related
dementias.

256
References
Abeyta, A. A., Routledge, C., & Kaslon, S. (2020). Combating Loneliness With Nostalgia:
Nostalgic Feelings Attenuate Negative Thoughts and Motivations Associated With
Loneliness. Frontiers in Psychology, 11. https://doi.org/10.3389/fpsyg.2020.01219
Abeyta, A. A., Routledge, C., Roylance, C., Wildschut, T., & Sedikides, C. (2015). Attachmentrelated avoidance and the social and agentic content of nostalgic memories. Journal of
Social and Personal Relationships, 32(3), 406–413.
https://doi.org/10.1177/0265407514533770
Abrams, D. A., Ryali, S., Chen, T., Chordia, P., Khouzam, A., Levitin, D. J., & Menon, V. (2013).
Inter-subject synchronization of brain responses during natural music listening. The
European Journal of Neuroscience, 37(9), 1458–1469. https://doi.org/10.1111/ejn.12173
Allen, A. P., Doyle, C., Commins, S., & Roche, R. A. P. (2018). Autobiographical memory, the
ageing brain and mechanisms of psychological interventions. Ageing Research Reviews,
42, 100–111. https://doi.org/10.1016/j.arr.2017.12.003
Allen, K.-A., Gray, D. L., Baumeister, R. F., & Leary, M. R. (2022). The Need to Belong: A
Deep Dive into the Origins, Implications, and Future of a Foundational Construct.
Educational Psychology Review, 34(2), 1133–1156. https://doi.org/10.1007/s10648-021-
09633-6
Alluri, V., Toiviainen, P., Lund, T. E., Wallentin, M., Vuust, P., Nandi, A. K., Ristaniemi, T., &
Brattico, E. (2013). From Vivaldi to Beatles and back: Predicting lateralized brain
responses to music. NeuroImage, 83, 627–636.
https://doi.org/10.1016/j.neuroimage.2013.06.064
Amer, T., Wynn, J. S., & Hasher, L. (2022). Cluttered memory representations shape cognition in
old age. Trends in Cognitive Sciences, 26(3), 255–267.
https://doi.org/10.1016/j.tics.2021.12.002
Andrews-Hanna, J. R., Grilli, M. D., & Irish, M. (2019). A Review and Reappraisal of the
Default Network in Normal Aging and Dementia. In Oxford Research Encyclopedia of
Psychology. https://doi.org/10.1093/acrefore/9780190236557.013.384
Anspach, C. K. (1934). Medical Dissertation on Nostalgia by Johannes Hofer, 1688. Bulletin of
the Institute of the History of Medicine, 2(6), 376–391.
Athanasopoulos, G., Eerola, T., Lahdelma, I., & Kaliakatsos-Papakostas, M. (2021). Harmonic
organisation conveys both universal and culture-specific cues for emotional expression in
music. PLOS ONE, 16(1), e0244964. https://doi.org/10.1371/journal.pone.0244964
Baird, A., Brancatisano, O., Gelding, R., & Thompson, W. F. (2018). Characterization of Music
and Photograph Evoked Autobiographical Memories in People with Alzheimer’s Disease.
Journal of Alzheimer’s Disease, 66(2), 693–706. https://doi.org/10.3233/JAD-180627

257
Barbara Jennings GSW, M., & Vance, D. (2002). The Short-Term Effects of Music Therapy on
Different Types of Agitation in Adults with Alzheimer’s. Activities, Adaptation & Aging,
26(4), 27–33. https://doi.org/10.1300/J016v26n04_03
Barrett, F. S., Grimm, K. J., Robins, R. W., Wildschut, T., Sedikides, C., & Janata, P. (2010).
Music-evoked nostalgia: Affect, memory, and personality. Emotion, 10(3), 390–403.
https://doi.org/10.1037/a0019006
Barrett, F. S., & Janata, P. (2016). Neural responses to nostalgia-evoking music modeled by
elements of dynamic musical structure and individual differences in affective traits.
Neuropsychologia, 91, 234–246. https://doi.org/10.1016/j.neuropsychologia.2016.08.012
Barrett, F. S., Robins, R. W., & Janata, P. (2013). A brief form of the Affective Neuroscience
Personality Scales. Psychological Assessment, 25, 826–843.
https://doi.org/10.1037/a0032576
Bartlett, J. C., & Snelus, P. (1980). Lifespan Memory for Popular Songs. The American Journal
of Psychology, 93(3), 551–560. https://doi.org/10.2307/1422730
Bartoń, K. (2023). MuMIn: Multi-Model Inference (R package version 1.47.5) [Computer
software]. https://CRAN.R-project.org/package=MuMIn
Batcho, K. I. (2013). Nostalgia: The bittersweet history of a psychological concept. History of
Psychology, 16, 165–176. https://doi.org/10.1037/a0032427
Bates, D. (n.d.). Lmer for SAS PROC MIXED Users.
Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting Linear Mixed-Effects Models
Using lme4. Journal of Statistical Software, 67, 1–48.
https://doi.org/10.18637/jss.v067.i01
Belden, A., Quinci, M. A., Geddes, M., Donovan, N. J., Hanser, S. B., & Loui, P. (2023).
Functional Organization of Auditory and Reward Systems in Aging. Journal of Cognitive
Neuroscience, 1–23. https://doi.org/10.1162/jocn_a_02028
Belfi, A. M., Bai, E., & Stroud, A. (2020). Comparing Methods for Analyzing Music-Evoked
Autobiographical Memories. Music Perception, 37(5), 392–402.
https://doi.org/10.1525/mp.2020.37.5.392
Belfi, A. M., Karlan, B., & Tranel, D. (2016). Music evokes vivid autobiographical memories.
Memory, 24(7), 979–989. https://doi.org/10.1080/09658211.2015.1061012
Belfi, A. M., Karlan, B., & Tranel, D. (2018). Damage to the medial prefrontal cortex impairs
music-evoked autobiographical memories. Psychomusicology: Music, Mind, and Brain,
28(4), 201–208. https://doi.org/10.1037/pmu0000222
Binder, J. R., & Desai, R. H. (2011). The neurobiology of semantic memory. Trends in Cognitive
Sciences, 15(11), 527–536. https://doi.org/10.1016/j.tics.2011.10.001

258
Blood, A. J., & Zatorre, R. J. (2001). Intensely pleasurable responses to music correlate with
activity in brain regions implicated in reward and emotion. Proceedings of the National
Academy of Sciences, 98(20), 11818–11823. https://doi.org/10.1073/pnas.191355898
Bonnici, H. M., Richter, F. R., Yazar, Y., & Simons, J. S. (2016). Multimodal Feature Integration
in the Angular Gyrus during Episodic and Semantic Retrieval. Journal of Neuroscience,
36(20), 5462–5471. https://doi.org/10.1523/JNEUROSCI.4310-15.2016
Boyd, R., Ashokkumar, A., Seraj, S., & Pennebaker, J. (2022). The Development and
Psychometric Properties of LIWC-22. https://doi.org/10.13140/RG.2.2.23890.43205
Brancatisano, O., Baird, A., & Thompson, W. F. (2020). Why is music therapeutic for
neurological disorders? The Therapeutic Music Capacities Model. Neuroscience &
Biobehavioral Reviews, 112, 600–615. https://doi.org/10.1016/j.neubiorev.2020.02.008
Buckner, R. L., Andrews-Hanna, J. R., & Schacter, D. L. (2008). The Brain’s Default Network.
Annals of the New York Academy of Sciences, 1124(1), 1–38.
https://doi.org/10.1196/annals.1440.011
Cabison, R. (2013, January 2). Billboard 200TM. Billboard.
https://www.billboard.com/charts/billboard-200/
Cambridge Dictionary. (2024). Nostalgia. In Cambridge Dictionary. Cambridge University
Press. https://dictionary.cambridge.org/us/dictionary/english/nostalgia
Carlson, E., Saarikallio, S., Toiviainen, P., Bogert, B., Kliuchko, M., & Brattico, E. (2015).
Maladaptive and adaptive emotion regulation through music: A behavioral and
neuroimaging study of males and females. Frontiers in Human Neuroscience, 9.
https://www.frontiersin.org/articles/10.3389/fnhum.2015.00466
Caulfield, K. (2019, October 6). The Beatles’ ‘Abbey Road’ Returns to Top 3 on Billboard 200
Chart After Nearly 50 Years. Billboard. https://www.billboard.com/pro/the-beatles-abbeyroad-returns-billboard-200-chart-top-3/
Celsis, P., Boulanouar, K., Doyon, B., Ranjeva, J. P., Berry, I., Nespoulous, J. L., & Chollet, F.
(1999). Differential fMRI Responses in the Left Posterior Superior Temporal Gyrus and
Left Supramarginal Gyrus to Habituation and Change Detection in Syllables and Tones.
NeuroImage, 9(1), 135–144. https://doi.org/10.1006/nimg.1998.0389
Chan, M. M. Y., & Han, Y. M. Y. (2022). The functional brain networks activated by music
listening: A neuroimaging meta-analysis and implications for treatment.
Neuropsychology, 36(1), 4–22. https://doi.org/10.1037/neu0000777
Chen, J., Layous, K., Wildschut, T., & Sedikides, C. (2023). Identifying nostalgia in text: The
development and validation of the nostalgia dictionary. Journal of Consumer Psychology,
33(4), 728–742. https://doi.org/10.1002/jcpy.1359

259
Cheung, W.-Y., Sedikides, C., & Wildschut, T. (2016). Induced nostalgia increases optimism (via
social-connectedness and self-esteem) among individuals high, but not low, in trait
nostalgia. Personality and Individual Differences, 90, 283–288.
https://doi.org/10.1016/j.paid.2015.11.028
Cheung, W.-Y., Wildschut, T., Sedikides, C., Hepper, E. G., Arndt, J., & Vingerhoets, A. J. J. M.
(2013). Back to the Future: Nostalgia Increases Optimism. Personality and Social
Psychology Bulletin, 39(11), 1484–1496. https://doi.org/10.1177/0146167213499187
Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155–159.
https://doi.org/10.1037//0033-2909.112.1.155
Conway, M. A., Wang, Q., Hanyu, K., & Haque, S. (2005). A Cross-Cultural Investigation of
Autobiographical Memory: On the Universality and Cultural Variation of the
Reminiscence Bump. Journal of Cross-Cultural Psychology, 36(6), 739–749.
https://doi.org/10.1177/0022022105280512
Cornell Kärnekull, S., Arshamian, A., Willander, J., Jönsson, F. U., Nilsson, M. E., & Larsson,
M. (2020). The reminiscence bump is blind to blindness: Evidence from sound- and odorevoked autobiographical memory. Consciousness and Cognition, 78, 102876.
https://doi.org/10.1016/j.concog.2019.102876
Cuddy, L. L., Sikka, R., Silveira, K., Bai, S., & Vanstone, A. (2017a). Music-evoked
autobiographical memories (MEAMs) in Alzheimer disease: Evidence for a positivity
effect. Cogent Psychology, 4(1), 1277578.
https://doi.org/10.1080/23311908.2016.1277578
Cuddy, L. L., Sikka, R., Silveira, K., Bai, S., & Vanstone, A. (2017b). Music-evoked
autobiographical memories (MEAMs) in Alzheimer disease: Evidence for a positivity
effect. Cogent Psychology, 4(1), 1277578.
https://doi.org/10.1080/23311908.2016.1277578
Cuddy, L. L., Sikka, R., & Vanstone, A. (2015). Preservation of musical memory and
engagement in healthy aging and Alzheimer’s disease. Annals of the New York Academy
of Sciences, 1337, 223–231. https://doi.org/10.1111/nyas.12617
Curci, A., Battista, F., Lanciano, T., d’Ovidio, F. D., & Conway, M. A. (2024). The reminiscence
bump and the self: Evidence from five studies on positive and negative memories.
Memory, 0(0), 1–19. https://doi.org/10.1080/09658211.2024.2325522
Davalos, S., Merchant, A., Rose, G. M., Lessley, B. J., & Teredesai, A. M. (2015). ‘The good old
days’: An examination of nostalgia in Facebook posts. International Journal of HumanComputer Studies, 83, 83–93. https://doi.org/10.1016/j.ijhcs.2015.05.009
Davey, C. G., Pujol, J., & Harrison, B. J. (2016). Mapping the self in the brain’s default mode
network. NeuroImage, 132, 390–397. https://doi.org/10.1016/j.neuroimage.2016.02.022

260
Davis, K. L., Panksepp, J., & Normansell, L. (2003). The Affective Neuroscience Personality
Scales: Normative Data and Implications. Neuropsychoanalysis, 5(1), 57–69.
https://doi.org/10.1080/15294145.2003.10773410
Davoodi, N. M., Dionne, L., Mor, V., & McCreedy, E. M. (2023). Personalization of
Reminiscence Therapies in the Nursing Home Setting: Lessons from an Embedded
Pragmatic Trial of a Music Intervention. Journal of the American Medical Directors
Association, 0(0). https://doi.org/10.1016/j.jamda.2023.04.027
Dimock, M. (2019). Defining generations: Where Millennials end and Generation Z begins. Pew
Research Center. https://www.pewresearch.org/short-reads/2019/01/17/wheremillennials-end-and-generation-z-begins/
Edwards, E., Hillaire-Clarke, C. S., Frankowski, D. W., Finkelstein, R., Cheever, T., Chen, W.
G., Onken, L., Poremba, A., Riddle, R., Schloesser, D., Burgdorf, C. E., Wells, N.,
Fleming, R., & Collins, F. S. (2023). NIH Music-Based Intervention Toolkit: MusicBased Interventions for Brain Disorders of Aging. Neurology.
https://doi.org/10.1212/WNL.0000000000206797
Eerola, T., & Vuoskoski, J. K. (2011). A comparison of the discrete and dimensional models of
emotion in music. Psychology of Music, 39(1), 18–49.
https://doi.org/10.1177/0305735610362821
El Haj, M., Fasotti, L., & Allain, P. (2012). The involuntary nature of music-evoked
autobiographical memories in Alzheimer’s disease. Consciousness and Cognition, 21,
238–246. https://doi.org/10.1016/j.concog.2011.12.005
Faber, S. E. M., Belden, A. G., Loui, P., & McIntosh, R. (2023). Age-related variability in
network engagement during music listening. Network Neuroscience (Cambridge, Mass.),
7(4), 1404–1419. https://doi.org/10.1162/netn_a_00333
Ferris, C. S., Inman, C. S., & Hamann, S. (2024). FMRI correlates of autobiographical memory:
Comparing silent retrieval with narrated retrieval. Neuropsychologia, 108842.
https://doi.org/10.1016/j.neuropsychologia.2024.108842
Fetterman, A. K., Evans, N. D., Ravey, E. P., Henderson, P. R., Tran, B. H. L., & Boyd, R. L.
(2024). The Topics of Nostalgic Recall: The Benefits of Nostalgia Depend on the Topics
That One Recalls. Social Psychological and Personality Science, 19485506241229305.
https://doi.org/10.1177/19485506241229305
Fischer, C. E., Churchill, N., Leggieri, M., Vuong, V., Tau, M., Fornazzari, L. R., Thaut, M. H.,
& Schweizer, T. A. (2021). Long-Known Music Exposure Effects on Brain Imaging and
Cognition in Early-Stage Cognitive Decline: A Pilot Study. Journal of Alzheimer’s
Disease: JAD, 84(2), 819–833. https://doi.org/10.3233/JAD-210610
Ford, J. H., Addis, D. R., & Giovanello, K. S. (2011). Differential neural activity during search of
specific and general autobiographical memories elicited by musical cues.

261
Neuropsychologia, 49(9), 2514–2526.
https://doi.org/10.1016/j.neuropsychologia.2011.04.032
Ford, J. H., Rubin, D. C., & Giovanello, K. S. (2016). The effects of song familiarity and age on
phenomenological characteristics and neural recruitment during autobiographical
memory retrieval. Psychomusicology, 26(3), 199–210.
https://doi.org/10.1037/pmu0000152
Foster, N. A., & Valentine, E. R. (2001). The Effect of Auditory Stimulation on Autobiographical
Recall in Dementia. Experimental Aging Research, 27(3), 215–228.
https://doi.org/10.1080/036107301300208664
Frankenbach, J., Wildschut, T., Juhl, J., & Sedikides, C. (2021). Does neuroticism disrupt the
psychological benefits of nostalgia? A meta-analytic test. European Journal of
Personality, 35(2), 249–266. https://doi.org/10.1002/per.2276
Freitas, C., Manzato, E., Burini, A., Taylor, M. J., Lerch, J. P., & Anagnostou, E. (2018). Neural
Correlates of Familiarity in Music Listening: A Systematic Review and a Neuroimaging
Meta-Analysis. Frontiers in Neuroscience, 0. https://doi.org/10.3389/fnins.2018.00686
Galdo-Alvarez, S., Lindín, M., & Díaz, F. (2009). Age-related prefrontal over-recruitment in
semantic memory retrieval: Evidence from successful face naming and the tip-of-thetongue state. Biological Psychology, 82(1), 89–96.
https://doi.org/10.1016/j.biopsycho.2009.06.003
Garrido, S. (2018). The influence of personality and coping style on the affective outcomes of
nostalgia: Is nostalgia a healthy coping mechanism or rumination? Personality and
Individual Differences, 120, 259–264. https://doi.org/10.1016/j.paid.2016.07.021
Garrido, S., & Davidson, J. W. (2019). Music, Nostalgia and Memory: Historical and
Psychological Perspectives. Springer International Publishing.
https://doi.org/10.1007/978-3-030-02556-4
Gasquoine, P. G. (2014). Contributions of the Insula to Cognition and Emotion. Neuropsychology
Review, 24(2), 77–87. https://doi.org/10.1007/s11065-014-9246-9
Gibbs, H., & Egermann, H. (2021). Music-Evoked Nostalgia and Wellbeing During the United
Kingdom COVID-19 Pandemic: Content, Subjective Effects, and Function. Frontiers in
Psychology, 12, 787. https://doi.org/10.3389/fpsyg.2021.647891
Gómez-Gallego, M., Gómez-Gallego, J. C., Gallego-Mellado, M., & García-García, J. (2021).
Comparative Efficacy of Active Group Music Intervention versus Group Music Listening
in Alzheimer’s Disease. International Journal of Environmental Research and Public
Health, 18(15), Article 15. https://doi.org/10.3390/ijerph18158067
Gordon, B. A., Tse, C.-Y., Gratton, G., & Fabiani, M. (2014). Spread of activation and
deactivation in the brain: Does age matter? Frontiers in Aging Neuroscience, 6.
https://doi.org/10.3389/fnagi.2014.00288

262
Gosling, S. D., Rentfrow, P. J., & Swann Jr., W. B. (2003). A very brief measure of the Big-Five
personality domains. Journal of Research in Personality, 37(6), 504–528.
https://doi.org/10.1016/S0092-6566(03)00046-1
Grady, C. L., Grigg, O., & Ng, C. (2012). Age differences in default and reward networks during
processing of personally relevant information. Neuropsychologia, 50(7), 1682–1697.
https://doi.org/10.1016/j.neuropsychologia.2012.03.024
Green, P., Mcleod, C., & Adlay, P. (2023). simr: An R package for power analysis of generalised
linear mixed models by simulation (1.0.7) [Computer software].
https://github.com/pitakakariki/simr
Grilli, M. D., & Sheldon, S. (2022). Autobiographical event memory and aging: Older adults get
the gist. Trends in Cognitive Sciences, 26(12), 1079–1089.
https://doi.org/10.1016/j.tics.2022.09.007
Haj, M. E., Postal, V., & Allain, P. (2012). Music Enhances Autobiographical Memory in Mild
Alzheimer’s Disease. Educational Gerontology, 38(1), 30–41.
https://doi.org/10.1080/03601277.2010.515897
Hanson, C., Anderton, J., Way, S. F., Anderson, I., Wolf, S., & Wang, A. (2022). Time after Time:
Longitudinal Trends in Nostalgic Listening. Proceedings of the International AAAI
Conference on Web and Social Media, 16, 311–322.
Harris, P. A., Taylor, R., Minor, B. L., Elliott, V., Fernandez, M., O’Neal, L., McLeod, L.,
Delacqua, G., Delacqua, F., Kirby, J., & Duda, S. N. (2019). The REDCap consortium:
Building an international community of software platform partners. Journal of
Biomedical Informatics, 95, 103208. https://doi.org/10.1016/j.jbi.2019.103208
Harris, P. A., Taylor, R., Thielke, R., Payne, J., Gonzalez, N., & Conde, J. G. (2009). Research
electronic data capture (REDCap)—A metadata-driven methodology and workflow
process for providing translational research informatics support. Journal of Biomedical
Informatics, 42(2), 377–381. https://doi.org/10.1016/j.jbi.2008.08.010
Hasson, U., Malach, R., & Heeger, D. J. (2010). Reliability of cortical activity during natural
stimulation. Trends in Cognitive Sciences, 14(1), 40–48.
https://doi.org/10.1016/j.tics.2009.10.011
He, W., Goodkind, D., & Kowal, P. (2016). An Aging World: 2015.
https://doi.org/10.13140/RG.2.1.1088.9362
Hepper, E. G., Ritchie, T. D., Sedikides, C., & Wildschut, T. (2012). Odyssey’s end: Lay
conceptions of nostalgia reflect its original homeric meaning. Emotion, 12(1), 102–119.
https://doi.org/10.1037/a0025167
Hepper, E. G., Sedikides, C., Wildschut, T., Cheung, W. Y., Abakoumkin, G., Arikan, G.,
Aveyard, M., Baldursson, E. B., Bialobrzeska, O., Bouamama, S., Bouzaouech, I.,
Brambilla, M., Burger, A. M., Chen, S. X., Cisek, S., Demassosso, D., Estevan-Reina, L.,

263
González Gutiérrez, R., Gu, L., … Zengel, B. (2024). Pancultural nostalgia in action:
Prevalence, triggers, and psychological functions of nostalgia across cultures. Journal of
Experimental Psychology. General. https://doi.org/10.1037/xge0001521
Hepper, E. G., Wildschut, T., Sedikides, C., Ritchie, T. D., Yung, Y.-F., Hansen, N., Abakoumkin,
G., Arikan, G., Cisek, S. Z., Demassosso, D. B., Gebauer, J. E., Gerber, J. P., González,
R., Kusumi, T., Misra, G., Rusu, M., Ryan, O., Stephan, E., Vingerhoets, A. J. J., & Zhou,
X. (2014). Pancultural nostalgia: Prototypical conceptions across cultures. Emotion,
14(4), 733–747. https://doi.org/10.1037/a0036790
Herzberg, P. Y., & Brähler, E. (2006). Assessing the Big-Five personality domains via short
forms: A cautionary note and a proposal. European Journal of Psychological Assessment,
22(3), 139–148. https://doi.org/10.1027/1015-5759.22.3.139
Holak, S. L., & Havlena, W. J. (1998). Feelings, Fantasies, and Memories: An Examination of
the Emotional Components of Nostalgia. Journal of Business Research, 42(3), 217–226.
https://doi.org/10.1016/S0148-2963(97)00119-7
Holbrook, M. B., & Schindler, R. M. (1989). Some Exploratory Findings on the Development of
Musical Tastes. Journal of Consumer Research, 16(1), 119–124.
https://doi.org/10.1086/209200
Hong, E. K., Sedikides, C., & Wildschut, T. (2021). How Does Nostalgia Conduce to Global
Self-Continuity? The Roles of Identity Narrative, Associative Links, and Stability.
Personality and Social Psychology Bulletin, 01461672211024889.
https://doi.org/10.1177/01461672211024889
Irish, M., Cunningham, C. J., Walsh, J. B., Coakley, D., Lawlor, B. A., Robertson, I. H., & Coen,
R. F. (2006). Investigating the enhancing effect of music on autobiographical memory in
mild Alzheimer’s disease. Dementia and Geriatric Cognitive Disorders, 22(1), 108–120.
https://doi.org/10.1159/000093487
Ismail, S., Dodd, E., Christopher, G., Wildschut, T., Sedikides, C., & Cheston, R. (2022). The
Content of Nostalgic Memories Among People Living with Dementia. The International
Journal of Aging and Human Development, 94(4), 436–458.
https://doi.org/10.1177/00914150211024185
Jackson, J. C., Watts, J., Henry, T. R., List, J.-M., Forkel, R., Mucha, P. J., Greenhill, S. J., Gray,
R. D., & Lindquist, K. A. (2019). Emotion semantics show both cultural variation and
universal structure. Science, 366(6472), 1517–1522.
https://doi.org/10.1126/science.aaw8160
Jacoby, N., Undurraga, E. A., McPherson, M. J., Valdés, J., Ossandón, T., & McDermott, J. H.
(2019). Universal and Non-universal Features of Musical Pitch Perception Revealed by
Singing. Current Biology, 29(19), 3229-3243.e12.
https://doi.org/10.1016/j.cub.2019.08.020

264
Jakubowski, K. (2019). Music-evoked autobiographical memories in everyday life.
https://doi.org/None
Jakubowski, K., Belfi, A. M., & Eerola, T. (2021). Phenomenological Differences in Music- and
Television-Evoked Autobiographical Memories. Music Perception, 38(5), 435–455.
https://doi.org/10.1525/mp.2021.38.5.435
Jakubowski, K., Eerola, T., Tillmann, B., Perrin, F., & Heine, L. (2020). A Cross-Sectional Study
of Reminiscence Bumps for Music-Related Memories in Adulthood. Music & Science, 3,
2059204320965058. https://doi.org/10.1177/2059204320965058
Jakubowski, K., & Ghosh, A. (2019). Music-evoked autobiographical memories in everyday life.
Psychology of Music, 0305735619888803. https://doi.org/10.1177/0305735619888803
Jakubowski, K., & Ghosh, A. (2021). Music-evoked autobiographical memories in everyday life.
Psychology of Music, 49(3), 649–666. https://doi.org/10.1177/0305735619888803
Janata, P. (2009). The Neural Architecture of Music-Evoked Autobiographical Memories.
Cerebral Cortex, 19(11), 2579–2594. https://doi.org/10.1093/cercor/bhp008
Janata, P. (2015). Chapter 11—Neural basis of music perception. In M. J. Aminoff, F. Boller, &
D. F. Swaab (Eds.), Handbook of Clinical Neurology (Vol. 129, pp. 187–205). Elsevier.
https://doi.org/10.1016/B978-0-444-62630-1.00011-1
Janata, P., Tomic, S. T., & Rakowski, S. K. (2007). Characterization of music-evoked
autobiographical memories. Memory (Hove, England), 15(8), 845–860.
https://doi.org/10.1080/09658210701734593
Janssen, S., Chessa, A., & Murre, J. (2005). The reminiscence bump in autobiographical
memory: Effects of age, gender, education, and culture. Memory, 13(6), 658–668.
https://doi.org/10.1080/09658210444000322
Jenkinson, M., Beckmann, C. F., Behrens, T. E. J., Woolrich, M. W., & Smith, S. M. (2012).
FSL. NeuroImage, 62(2), 782–790. https://doi.org/10.1016/j.neuroimage.2011.09.015
Juhl, J., & Biskas, M. (2023). Nostalgia: An impactful social emotion. Current Opinion in
Psychology, 49, 101545. https://doi.org/10.1016/j.copsyc.2022.101545
Juhl, J., Routledge, C., Arndt, J., Sedikides, C., & Wildschut, T. (2010). Fighting the future with
the past: Nostalgia buffers existential threat. Journal of Research in Personality, 44(3),
309–314. https://doi.org/10.1016/j.jrp.2010.02.006
Kaiser, A. P., & Berntsen, D. (2023). The cognitive characteristics of music-evoked
autobiographical memories: Evidence from a systematic review of clinical investigations.
WIREs Cognitive Science, 14(3), e1627. https://doi.org/10.1002/wcs.1627
Kaplan, H. A. (1987). The psychopathology of nostalgia. Psychoanalytic Review, 74(4), 465–
486.

265
Kaufman, Y., Anaki, D., Binns, M., & Freedman, M. (2007). Cognitive decline in Alzheimer
disease: Impact of spirituality, religiosity, and QOL. Neurology, 68(18), 1509–1514.
https://doi.org/10.1212/01.wnl.0000260697.66617.59
Kazak, A. E. (2018). Editorial: Journal article reporting standards. The American Psychologist,
73(1), 1–2. https://doi.org/10.1037/amp0000263
Kehoe, E. G., Toomey, J. M., Balsters, J. H., & Bokde, A. L. W. (2013). Healthy aging is
associated with increased neural processing of positive valence but attenuated processing
of emotional arousal: An fMRI study. Neurobiology of Aging, 34(3), 809–821.
https://doi.org/10.1016/j.neurobiolaging.2012.07.006
Kikuchi, Y., & Noriuchi, M. (2017). The Nostalgic Brain: Its Neural Basis and Positive
Emotional Role in Resilience. In S. Fukuda (Ed.), Emotional Engineering, Vol.5 (pp. 43–
53). Springer International Publishing. https://doi.org/10.1007/978-3-319-53195-3_5
Koelsch, S. (2015). Music-evoked emotions: Principles, brain correlates, and implications for
therapy: Functional neuroanatomy of music-evoked emotions. Annals of the New York
Academy of Sciences, 1337(1), 193–201. https://doi.org/10.1111/nyas.12684
Koelsch, S. (2020). A coordinate-based meta-analysis of music-evoked emotions. NeuroImage,
223, 117350. https://doi.org/10.1016/j.neuroimage.2020.117350
Krumhansl, C. L., & Zupnick, J. A. (2013). Cascading Reminiscence Bumps in Popular Music.
Psychological Science, 24(10), 2057–2068. https://doi.org/10.1177/0956797613486486
Kubit, B., & Janata, P. (2018). Listening for memories: Attentional focus dissociates functional
brain networks engaged by memory-evoking music. Psychomusicology: Music, Mind,
and Brain, 28(2), 82–100. https://doi.org/10.1037/pmu0000210
Kumar, M., Anderson, M. J., Antony, J. W., Baldassano, C., Brooks, P. P., Cai, M. B., Chen, P.-H.
C., Ellis, C. T., Henselman-Petrusek, G., Huberdeau, D., Hutchinson, J. B., Li, Y. P., Lu,
Q., Manning, J. R., Mennen, A. C., Nastase, S. A., Richard, H., Schapiro, A. C., Schuck,
N. W., … Norman, K. A. (2021). BrainIAK: The Brain Imaging Analysis Kit. Aperture
Neuro, 1(4), 10.52294/31bb5b68-2184-411b-8c00-a1dacb61e1da.
https://doi.org/10.52294/31bb5b68-2184-411b-8c00-a1dacb61e1da
Kumar, M., Ellis, C. T., Lu, Q., Zhang, H., Capotă, M., Willke, T. L., Ramadge, P. J., TurkBrowne, N. B., & Norman, K. A. (2020). BrainIAK tutorials: User-friendly learning
materials for advanced fMRI analysis. PLOS Computational Biology, 16(1), e1007549.
https://doi.org/10.1371/journal.pcbi.1007549
Kwak, J., Anderson, K., & O’Connell Valuch, K. (2020). Findings From a Prospective
Randomized Controlled Trial of an Individualized Music Listening Program for Persons
With Dementia. Journal of Applied Gerontology: The Official Journal of the Southern
Gerontological Society, 39(6), 567–575. https://doi.org/10.1177/0733464818778991

266
Laiho, S. (2004). The Psychological Functions of Music in Adolescence. Nordic Journal of
Music Therapy, 13(1), 47–63. https://doi.org/10.1080/08098130409478097
Lamere, P. (n.d.). Spotipy: A light weight Python library for the Spotify Web API [Computer
software]. https://github.com/plamere/spotipy
Lammel, S., Lim, B. K., Ran, C., Huang, K. W., Betley, M. J., Tye, K. M., Deisseroth, K., &
Malenka, R. C. (2012). Input-specific control of reward and aversion in the ventral
tegmental area. Nature, 491(7423), 212–217. https://doi.org/10.1038/nature11527
Larsen, J. T., Hershfield, Hal. E., Stastny, B. J., & Hester, N. (2017). On the relationship between
positive and negative affect: Their correlation and their co-occurrence. Emotion, 17(2),
323–336. https://doi.org/10.1037/emo0000231
Leary, M. R., Kelly, K. M., Cottrell, C. A., & Schreindorfer, L. S. (2013). Construct Validity of
the Need to Belong Scale: Mapping the Nomological Network. Journal of Personality
Assessment, 95(6), 610–624. https://doi.org/10.1080/00223891.2013.819511
Leary, R. F. B., Mark R. (2007). The Need to Belong: Desire for Interpersonal Attachments as a
Fundamental Human Motivation. In Interpersonal Development. Routledge.
Leggieri, M., Fornazzari, L., Thaut, M., Barfett, J., Munoz, D. G., Schweizer, T. A., & Fischer, C.
(2018). Determining the Impact of Passive Music Exposure on Brain Activation and
Functional Connectivity Using FMRI in Patients with Early Alzheimer’s Disease. The
American Journal of Geriatric Psychiatry, 26(3), S135.
https://doi.org/10.1016/j.jagp.2018.01.164
Leipold, B., & Loepthien, T. (2015). Music reception and emotional regulation in adolescence
and adulthood. Musicae Scientiae, 19(1), 111–128.
https://doi.org/10.1177/1029864915570354
Lennon, J. C., Aita, S. L., Del Bene, V. A., Rhoads, T., Resch, Z. J., Eloi, J. M., & Walker, K. A.
(2022). Black and white individuals differ in dementia prevalence, risk factors, and
symptomatic presentation. Alzheimer’s & Dementia : The Journal of the Alzheimer’s
Association, 18(8), 1461–1471. https://doi.org/10.1002/alz.12509
Lenth, R. (2023). emmeans: Estimated Marginal Means, aka Least-Squares Means [Computer
software]. https://CRAN.R-project.org/package=emmeans
Leunissen, J., Wildschut, T., Sedikides, C., & Routledge, C. (2021). The Hedonic Character of
Nostalgia: An Integrative Data Analysis. Emotion Review, 13(2), 139–156.
https://doi.org/10.1177/1754073920950455
Levine, B., Svoboda, E., Hay, J. F., Winocur, G., & Moscovitch, M. (2002). Aging and
autobiographical memory: Dissociating episodic from semantic retrieval. Psychology and
Aging, 17(4), 677–689. https://doi.org/10.1037/0882-7974.17.4.677

267
Lineweaver, T. T., Bergeson, T. R., Ladd, K., Johnson, H., Braid, D., Ott, M., Hay, D. P., Plewes,
J., Hinds, M., LaPradd, M. L., Bolander, H., Vitelli, S., Lain, M., & Brimmer, T. (2021).
The Effects of Individualized Music Listening on Affective, Behavioral, Cognitive, and
Sundowning Symptoms of Dementia in Long-Term Care Residents. Journal of Aging and
Health, 08982643211033407. https://doi.org/10.1177/08982643211033407
Lord, T. R., & Garner, J. E. (1993). Effects of music on Alzheimer patients. Perceptual and
Motor Skills, 76(2), 451–455. https://doi.org/10.2466/pms.1993.76.2.451
Loureiro, C., van der Meulen, K., & del Barrio, C. (2024). Why I listen to music: Emotion
regulation and identity construction through music in mid-adolescence. Empiria: Revista
de Metodología de Ciencias Sociales, 60, 145–168.
Madoglou, A., Gkinopoulos, T., Xanthopoulos, P., & Kalamaras, D. (2017).
REPRESENTATIONS OF AUTOBIOGRAPHICAL NOSTALGIC MEMORIES:
GENERATIONAL EFFECT, GENDER, NOSTALGIA PRONENESS AND
COMMUNICATION OF NOSTALGIC EXPERIENCES. 7, 29.
Malach, R., Reppas, J. B., Benson, R. R., Kwong, K. K., Jiang, H., Kennedy, W. A., Ledden, P.
J., Brady, T. J., Rosen, B. R., & Tootell, R. B. (1995). Object-related activity revealed by
functional magnetic resonance imaging in human occipital cortex. Proceedings of the
National Academy of Sciences, 92(18), 8135–8139.
https://doi.org/10.1073/pnas.92.18.8135
Markowitz, D. M. (2021). The Meaning Extraction Method: An Approach to Evaluate Content
Patterns From Large-Scale Language Data. Frontiers in Communication, 6.
https://doi.org/10.3389/fcomm.2021.588823
Martins, B., Ponzio, A., Velasco, R., Kaplan, J., & Mather, M. (2015). Dedifferentiation of
emotion regulation strategies in the aging brain. Social Cognitive and Affective
Neuroscience, 10(6), 840–847. https://doi.org/10.1093/scan/nsu129
Mather, M. (2012). The emotion paradox in the aging brain. Annals of the New York Academy of
Sciences, 1251(1), 33–49. https://doi.org/10.1111/j.1749-6632.2012.06471.x
Mather, M., & Carstensen, L. L. (2005). Aging and motivated cognition: The positivity effect in
attention and memory. Trends in Cognitive Sciences, 9(10), 496–502.
https://doi.org/10.1016/j.tics.2005.08.005
Matsunaga, M., Bai, Y., Yamakawa, K., Toyama, A., Kashiwagi, M., Fukuda, K., Oshida, A.,
Sanada, K., Fukuyama, S., Shinoda, J., Yamada, J., Sadato, N., & Ohira, H. (2013).
Brain–Immune Interaction Accompanying Odor-Evoked Autobiographic Memory. PLoS
ONE, 8(8), e72523. https://doi.org/10.1371/journal.pone.0072523
Matsunaga, M., Isowa, T., Yamakawa, K., Kawanishi, Y., Tsuboi, H., Kaneko, H., Sadato, N.,
Oshida, A., Katayama, A., Kashiwagi, M., & Ohira, H. (2011). Psychological and
physiological responses to odor-evoked autobiographic memory.

268
Matthews, G., Jones, D. M., & Chamberlain, A. G. (1990). Refining the measurement of mood:
The UWIST Mood Adjective Checklist. British Journal of Psychology, 81(1), 17–42.
https://doi.org/10.1111/j.2044-8295.1990.tb02343.x
Matziorinis, A. M., & Koelsch, S. (2022). The promise of music therapy for Alzheimer’s disease:
A review. Annals of the New York Academy of Sciences, 1516(1), 11–17.
https://doi.org/10.1111/nyas.14864
McAdams, D. P., Reynolds, J., Lewis, M., Patten, A. H., & Bowman, P. J. (2001). When Bad
Things Turn Good and Good Things Turn Bad: Sequences of Redemption and
Contamination in Life Narrative and their Relation to Psychosocial Adaptation in Midlife
Adults and in Students. Personality and Social Psychology Bulletin, 27(4), 474–485.
https://doi.org/10.1177/0146167201274008
McCreedy, E. M., Sisti, A., Gutman, R., Dionne, L., Rudolph, J. L., Baier, R., Thomas, K. S.,
Olson, M. B., Zediker, E. E., Uth, R., Shield, R. R., & Mor, V. (2022). Pragmatic Trial of
Personalized Music for Agitation and Antipsychotic Use in Nursing Home Residents
With Dementia. Journal of the American Medical Directors Association, 23(7), 1171–
1177. https://doi.org/10.1016/j.jamda.2021.12.030
McCreedy, E. M., Yang, X., Baier, R. R., Rudolph, J. L., Thomas, K. S., & Mor, V. (2019).
Measuring Effects of Nondrug Interventions on Behaviors: Music & Memory Pilot Study.
Journal of the American Geriatrics Society, 67(10), 2134–2138.
https://doi.org/10.1111/jgs.16069
Mehnert, T. (2018). Nostalgia and Music-Evoked Autobiographical Memories: Salience and
Content in Musicians vs. Non-Musicians. Honors Theses.
https://scholarworks.wmich.edu/honors_theses/3181
Mehr, S. A., Singh, M., Knox, D., Ketter, D. M., Pickens-Jones, D., Atwood, S., Lucas, C.,
Jacoby, N., Egner, A. A., Hopkins, E. J., Howard, R. M., Hartshorne, J. K., Jennings, M.
V., Simson, J., Bainbridge, C. M., Pinker, S., O’Donnell, T. J., Krasnow, M. M., &
Glowacki, L. (2019). Universality and diversity in human song. Science, 366(6468),
eaax0868. https://doi.org/10.1126/science.aax0868
Meilán García, J. J., Iodice, R., Carro, J., Sánchez, J. A., Palmero, F., & Mateos, A. M. (2012).
Improvement of autobiographic memory recovery by means of sad music in Alzheimer’s
Disease type dementia. Aging Clinical and Experimental Research, 24(3), 227–232.
https://doi.org/10.3275/7874
Mellor, D., Stokes, M., Firth, L., Hayashi, Y., & Cummins, R. (2008). Need for belonging,
relationship satisfaction, loneliness, and life satisfaction. Personality and Individual
Differences, 45(3), 213–218. https://doi.org/10.1016/j.paid.2008.03.020
Menon, V., & Levitin, D. J. (2005). The rewards of music listening: Response and physiological
connectivity of the mesolimbic system. NeuroImage, 28(1), 175–184.
https://doi.org/10.1016/j.neuroimage.2005.05.053

269
Menon, V., & Uddin, L. Q. (2010). Saliency, switching, attention and control: A network model
of insula function. Brain Structure and Function, 214(5), 655–667.
https://doi.org/10.1007/s00429-010-0262-0
Merriam-Webster. (2024). Definition of NOSTALGIA. In Merriam-Webster Dictionary.
http://www.merriam-webster.com/dictionary/nostalgia
Mesquita, B., Boiger, M., & De Leersnyder, J. (2016). The cultural construction of emotions.
Current Opinion in Psychology, 8, 31–36. https://doi.org/10.1016/j.copsyc.2015.09.015
Munawar, K., Kuhn, S. K., & Haque, S. (2018). Understanding the reminiscence bump: A
systematic review. PLOS ONE, 13(12), e0208595.
https://doi.org/10.1371/journal.pone.0208595
Nakano, T., Kato, M., Morito, Y., Itoi, S., & Kitazawa, S. (2013). Blink-related momentary
activation of the default mode network while viewing videos. Proceedings of the
National Academy of Sciences of the United States of America, 110(2), 702–706.
https://doi.org/10.1073/pnas.1214804110
Nápoles, A. M., & Chadiha, L. A. (2011). Advancing the Science of Recruitment and Retention
of Ethnically Diverse Populations. The Gerontologist, 51(suppl_1), S142–S146.
https://doi.org/10.1093/geront/gnr019
Nasreddine, Z. S., Phillips, N. A., Bédirian, V., Charbonneau, S., Whitehead, V., Collin, I.,
Cummings, J. L., & Chertkow, H. (2005). The Montreal Cognitive Assessment, MoCA: A
Brief Screening Tool For Mild Cognitive Impairment. Journal of the American Geriatrics
Society, 53(4), 695–699. https://doi.org/10.1111/j.1532-5415.2005.53221.x
Newman, D. B. (2022). Low Income Amplifies the Negative Relationship Between Nostalgia
Proneness and Well-Being. Applied Research in Quality of Life, 17(6), 3311–3326.
https://doi.org/10.1007/s11482-022-10066-8
Newman, D. B., & Sachs, M. E. (2020). The Negative Interactive Effects of Nostalgia and
Loneliness on Affect in Daily Life. Frontiers in Psychology, 11.
https://www.frontiersin.org/article/10.3389/fpsyg.2020.02185
Newman, D. B., & Sachs, M. E. (2023). Variation in bittersweet nostalgic feelings and their
divergent effects on daily well-being. Emotion, 23(4), 937–948.
https://doi.org/10.1037/emo0001137
Newman, D. B., Sachs, M. E., Stone, A. A., & Schwarz, N. (2020). Nostalgia and Well-Being in
Daily Life: An Ecological Validity Perspective. Journal of Personality and Social
Psychology, 118(2), 325–347. https://doi.org/10.1037/pspp0000236
Nilearn contributors. (n.d.). Nilearn [Computer software]. https://github.com/nilearn/nilearn

270
Nummenmaa, L., Glerean, E., Hari, R., & Hietanen, J. K. (2014). Bodily maps of emotions.
Proceedings of the National Academy of Sciences, 111(2), 646–651.
https://doi.org/10.1073/pnas.1321664111
Oba, K., Noriuchi, M., Atomi, T., Moriguchi, Y., & Kikuchi, Y. (2015). Memory and reward
systems coproduce ‘nostalgic’ experiences in the brain. Social Cognitive and Affective
Neuroscience, nsv073. https://doi.org/10.1093/scan/nsv073
Palan, S., & Schitter, C. (2018). Prolific.ac—A subject pool for online experiments. Journal of
Behavioral and Experimental Finance, 17, 22–27.
https://doi.org/10.1016/j.jbef.2017.12.004
Pennebaker, J., Booth, R., & Francis, M. (2001). Linguistic Inquiry and Word Count: Liwc2001.
Mahway: 837 Lawrence Erlbaum Assoc., 71.
Platz, F., Kopiez, R., Hasselhorn, J., & Wolf, A. (2015). The impact of song-specific age and
affective qualities of popular songs on music-evoked autobiographical memories
(MEAMs). Musicae Scientiae, 19(4), 327–349.
https://doi.org/10.1177/1029864915597567
Posner, J., Russell, J. A., & Peterson, B. S. (2005). The circumplex model of affect: An
integrative approach to affective neuroscience, cognitive development, and
psychopathology. Development and Psychopathology, 17(3), 715–734.
https://doi.org/10.1017/S0954579405050340
Presti, P., Ruzzon, D., Avanzini, P., Caruana, F., Rizzolatti, G., & Vecchiato, G. (2022).
Measuring arousal and valence generated by the dynamic experience of architectural
forms in virtual environments. Scientific Reports, 12(1), Article 1.
https://doi.org/10.1038/s41598-022-17689-9
Qin, P., & Northoff, G. (2011). How is our self related to midline regions and the default-mode
network? NeuroImage, 57(3), 1221–1233.
https://doi.org/10.1016/j.neuroimage.2011.05.028
Qualtrics (August 2022). (2022). [Computer software]. Qualtrics. www.qualtrics.com
Quinci, M. A., Belden, A., Goutama, V., Gong, D., Hanser, S., Donovan, N. J., Geddes, M., &
Loui, P. (2022). Longitudinal changes in auditory and reward systems following receptive
music-based intervention in older adults. Scientific Reports, 12(1), Article 1.
https://doi.org/10.1038/s41598-022-15687-5
R Core Team. (2021). R: A language and environment for statistical computing. [Computer
software]. https://www.R-project.org/
R Core Team. (2023). R: A Language and Environment for Statistical Computing [Computer
software]. R Foundation for Statistical Computing. https://www.R-project.org/

271
Ramanan, S., Piguet, O., & Irish, M. (2018). Rethinking the Role of the Angular Gyrus in
Remembering the Past and Imagining the Future: The Contextual Integration Model. The
Neuroscientist, 24(4), 342–352. https://doi.org/10.1177/1073858417735514
Rathbone, C. J., O’Connor, A. R., & Moulin, C. J. A. (2017). The tracks of my years: Personal
significance contributes to the reminiscence bump. Memory & Cognition, 45(1), 137–
150. https://doi.org/10.3758/s13421-016-0647-2
Regev, M., Halpern, A. R., Owen, A. M., Patel, A. D., & Zatorre, R. J. (2021). Mapping Specific
Mental Content during Musical Imagery. Cerebral Cortex, 31(8), 3622–3640.
https://doi.org/10.1093/cercor/bhab036
Reid, C. A., Green, J. D., Wildschut, T., & Sedikides, C. (2015). Scent-evoked nostalgia.
Memory, 23(2), 157–166. https://doi.org/10.1080/09658211.2013.876048
Ren, Y., & Brown, T. I. (2023). Beyond the ears: A review exploring the interconnected brain
behind the hierarchical memory of music. Psychonomic Bulletin & Review.
https://doi.org/10.3758/s13423-023-02376-1
Rentfrow, P. J., Goldberg, L. R., & Levitin, D. J. (2011). The Structure of Musical Preferences: A
Five-Factor Model. Journal of Personality and Social Psychology, 100(6), 1139–1157.
https://doi.org/10.1037/a0022406
Renwick, J., & Woolhouse, M. H. (2023). Reminiscence bump invariance with respect to genre,
age, and country. Psychology of Music, 03057356221141735.
https://doi.org/10.1177/03057356221141735
Reschke-Hernández, A. E., Belfi, A. M., Guzmán-Vélez, E., & Tranel, D. (2020). Hooked on a
Feeling: Influence of Brief Exposure to Familiar Music on Feelings of Emotion in
Individuals with Alzheimer’s Disease. Journal of Alzheimer’s Disease, 78(3), 1019–1031.
https://doi.org/10.3233/JAD-200889
Reuter-Lorenz, P. A., & Lustig, C. (2005). Brain aging: Reorganizing discoveries about the aging
mind. Current Opinion in Neurobiology, 15(2), 245–251.
https://doi.org/10.1016/j.conb.2005.03.016
Reybrouck, M., Vuust, P., & Brattico, E. (2018). Brain Connectivity Networks and the Aesthetic
Experience of Music. Brain Sciences, 8(6), Article 6.
https://doi.org/10.3390/brainsci8060107
Rossato-Bennett, M. (Director). (2014). Alive inside [documentary]. Projector Media BOND/360
City Drive Films.
Routledge, C., Arndt, J., Sedikides, C., & Wildschut, T. (2008). A blast from the past: The terror
management function of nostalgia. Journal of Experimental Social Psychology, 44(1),
132–140. https://doi.org/10.1016/j.jesp.2006.11.001

272
Routledge, C., Wildschut, T., Sedikides, C., Juhl, J., & Arndt, J. (2012). The power of the past:
Nostalgia as a meaning-making resource. Memory, 20(5), 452–460.
https://doi.org/10.1080/09658211.2012.677452
Rubin, D. C., & Schulkind, M. D. (1997). Distribution of important and word-cued
autobiographical memories in 20-, 35-, and 70-year-old adults. Psychology and Aging,
12, 524–535. https://doi.org/10.1037/0882-7974.12.3.524
Rusting, C. L., & Larsen, R. J. (1997). Extraversion, neuroticism, and susceptibility to positive
and negative affect: A test of two theoretical models. Personality and Individual
Differences, 22(5), 607–612. https://doi.org/10.1016/S0191-8869(96)00246-2
Saarikallio, S. (2006). Differences in adolescents’ use of music in mood regula- tion.
Saarikallio, S. (2012). Development and Validation of the Brief Music in Mood Regulation Scale
(B-MMR). Music Perception, 30(1), 97–105. https://doi.org/10.1525/mp.2012.30.1.97
Saarikallio, S., Alluri, V., Maksimainen, J., & Toiviainen, P. (2020). Emotions of music listening
in Finland and in India: Comparison of an individualistic and a collectivistic culture.
Psychology of Music, 0305735620917730. https://doi.org/10.1177/0305735620917730
Saarikallio, S., Nieminen, S., & Brattico, E. (2013). Affective reactions to musical stimuli reflect
emotional use of music in everyday life. Musicae Scientiae, 17(1), 27–39.
https://doi.org/10.1177/1029864912462381
Sachs, M. E., Damasio, A., & Habibi, A. (2015). The pleasures of sad music: A systematic
review. Frontiers in Human Neuroscience, 9, 404.
https://doi.org/10.3389/fnhum.2015.00404
Sachs, M. E., Habibi, A., Damasio, A., & Kaplan, J. T. (2020). Dynamic intersubject neural
synchronization reflects affective responses to sad music. NeuroImage, 218, 116512.
https://doi.org/10.1016/j.neuroimage.2019.116512
Sakka, L. S., & Saarikallio, S. (2020). Spontaneous Music-Evoked Autobiographical Memories
in Individuals Experiencing Depression. Music & Science, 3, 2059204320960575.
https://doi.org/10.1177/2059204320960575
Salimpoor, V. N., & Zatorre, R. J. (2013). Neural interactions that give rise to musical pleasure.
Psychology of Aesthetics, Creativity, and the Arts, 7(1), 62–75.
https://doi.org/10.1037/a0031819
Schaefer, H.-E. (2017). Music-Evoked Emotions—Current Studies. Frontiers in Neuroscience,
11. https://www.frontiersin.org/articles/10.3389/fnins.2017.00600
Schimmack, U. (2001). Pleasure, displeasure, and mixed feelings: Are semantic opposites
mutually exclusive? Cognition and Emotion, 15(1), 81–97.
https://doi.org/10.1080/02699930126097

273
Schubert, E. (2016). Does recall of a past music event invoke a reminiscence bump in young
adults? Memory, 24(7), 1007–1014. https://doi.org/10.1080/09658211.2015.1061014
Schult, J., Schneider, R., & Sparfeldt, J. R. (2019). Assessing personality with multi-descriptor
items: More harm than good? European Journal of Psychological Assessment, 35(1),
117–125. https://doi.org/10.1027/1015-5759/a000368
Sedikides, C., Leunissen, J., & Wildschut, T. (2022). The psychological benefits of music-evoked
nostalgia. Psychology of Music, 50(6), 2044–2062.
https://doi.org/10.1177/03057356211064641
Sedikides, C., & Wildschut, T. (2019). The sociality of personal and collective nostalgia.
European Review of Social Psychology, 30(1), 123–173.
https://doi.org/10.1080/10463283.2019.1630098
Sedikides, C., Wildschut, T., Arndt, J., & Routledge, C. (2008). Nostalgia Past, Present, and
Future. Current Directions in Psychological Science, 17(5), 304–307.
https://doi.org/10.1111/j.1467-8721.2008.00595.x
Sedikides, C., Wildschut, T., Cheung, W.-Y., Routledge, C., Hepper, E. G., Arndt, J., Vail, K.,
Zhou, X., Brackstone, K., & Vingerhoets, A. J. J. M. (2016). Nostalgia fosters selfcontinuity: Uncovering the mechanism (social connectedness) and consequence
(eudaimonic well-being). Emotion, 16(4), 524–539. https://doi.org/10.1037/emo0000136
Sedikides, C., Wildschut, T., Gaertner, L., Routledge, C., & Arndt, J. (2008). Nostalgia as enabler
of self continuity. In Self continuity: Individual and collective perspectives (pp. 227–239).
Psychology Press.
Sedikides, C., Wildschut, T., Routledge, C., Arndt, J., Hepper, E. G., & Zhou, X. (2015a).
Chapter Five - To Nostalgize: Mixing Memory with Affect and Desire. In J. M. Olson &
M. P. Zanna (Eds.), Advances in Experimental Social Psychology (Vol. 51, pp. 189–273).
Academic Press. https://doi.org/10.1016/bs.aesp.2014.10.001
Sedikides, C., Wildschut, T., Routledge, C., Arndt, J., Hepper, E. G., & Zhou, X. (2015b). To
Nostalgize. In Advances in Experimental Social Psychology (Vol. 51, pp. 189–273).
Elsevier. https://doi.org/10.1016/bs.aesp.2014.10.001
Seehusen, J., Cordaro, F., Wildschut, T., Sedikides, C., Routledge, C., Blackhart, G. C., Epstude,
K., & Vingerhoets, A. J. J. M. (2013). Individual differences in nostalgia proneness: The
integrating role of the need to belong. Personality and Individual Differences, 55(8),
904–908. https://doi.org/10.1016/j.paid.2013.07.020
Seghier, M. L. (2013). The Angular Gyrus: Multiple Functions and Multiple Subdivisions. The
Neuroscientist, 19(1), 43–61. https://doi.org/10.1177/1073858412440596
Sharpe, J. P., Martin, N. R., & Roth, K. A. (2011). Optimism and the Big Five factors of
personality: Beyond Neuroticism and Extraversion. Personality and Individual
Differences, 51(8), 946–951. https://doi.org/10.1016/j.paid.2011.07.033

274
Sheng, J., Wang, S., Zhang, L., Liu, C., Shi, L., Zhou, Y., Hu, H., Chen, C., & Xue, G. (2023).
Intersubject similarity in neural representations underlies shared episodic memory
content. Proceedings of the National Academy of Sciences, 120(35), e2308951120.
https://doi.org/10.1073/pnas.2308951120
Shifriss, R., Bodner, E., & Palgi, Y. (2015). When you’re down and troubled: Views on the
regulatory power of music. Psychology of Music, 43(6), 793–807.
https://doi.org/10.1177/0305735614540360
Shomstein, S. (2012). Cognitive functions of the posterior parietal cortex: Top-down and bottomup attentional control. Frontiers in Integrative Neuroscience, 6.
https://doi.org/10.3389/fnint.2012.00038
Spreng, R. N., Mar, R. A., & Kim, A. S. N. (2009). The Common Neural Basis of
Autobiographical Memory, Prospection, Navigation, Theory of Mind, and the Default
Mode: A Quantitative Meta-analysis. Journal of Cognitive Neuroscience, 21(3), 489–510.
https://doi.org/10.1162/jocn.2008.21029
Steenhaut, P., Demeyer, I., De Raedt, R., & Rossi, G. (2018). The Role of Personality in the
Assessment of Subjective and Physiological Emotional Reactivity: A Comparison
Between Younger and Older Adults. Assessment, 25(3), 285–301.
https://doi.org/10.1177/1073191117719510
Stephan, E., Wildschut, T., Sedikides, C., Zhou, X., He, W., Routledge, C., Cheung, W.-Y., &
Vingerhoets, A. J. J. M. (2014). The mnemonic mover: Nostalgia regulates avoidance and
approach motivation. Emotion, 14(3), 545–561. https://doi.org/10.1037/a0035673
Sterenberg, A. (2018). The Determinants of Affect, Emotional Arousal, and Autobiographical
Memories on Music-Evoked Nostalgia. Masters Theses.
https://scholarworks.wmich.edu/masters_theses/3794
Summerfield, J. J., Hassabis, D., & Maguire, E. A. (2009). Cortical midline involvement in
autobiographical memory. Neuroimage, 44(3), 1188–1200.
https://doi.org/10.1016/j.neuroimage.2008.09.033
Thaut, M. H., Fischer, C. E., Leggieri, M., Vuong, V., Churchill, N. W., Fornazzari, L. R., &
Schweizer, T. A. (2020). Neural Basis of Long-term Musical Memory in Cognitively
Impaired Older Persons. Alzheimer Disease & Associated Disorders, 34(3), 267–271.
https://doi.org/10.1097/WAD.0000000000000382
The Beatles. (1967). A day in the life: Vol. Remastered 2010.
https://open.spotify.com/embed/track/0hKRSZhUGEhKU6aNSPBACZ
The MathWorks Inc. (2022). MATLAB (9.13.0 (R2022b)) [Computer software]. The MathWorks
Inc.
Thomas, K. S., Baier, R., Kosar, C., Ogarek, J., Trepman, A., & Mor, V. (2017). Individualized
Music Program is Associated with Improved Outcomes for U.S. Nursing Home Residents

275
with Dementia. The American Journal of Geriatric Psychiatry: Official Journal of the
American Association for Geriatric Psychiatry, 25(9), 931–938.
https://doi.org/10.1016/j.jagp.2017.04.008
Thompson, M. M., Zanna, M. P., & Griffin, D. W. (1995). Let’s not be indifferent about
(attitudinal) ambivalence. In Attitude strength: Antecedents and consequences (pp. 361–
386). Lawrence Erlbaum Associates, Inc.
Thørrisen, M. M., & Sadeghi, T. (2023). The Ten-Item Personality Inventory (TIPI): A scoping
review of versions, translations and psychometric properties. Frontiers in Psychology, 14.
https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2023.1202953
Tibon, R., Fuhrmann, D., Levy, D. A., Simons, J. S., & Henson, R. N. (2019). Multimodal
Integration and Vividness in the Angular Gyrus During Episodic Encoding and Retrieval.
Journal of Neuroscience, 39(22), 4365–4374. https://doi.org/10.1523/JNEUROSCI.2102-
18.2018
Trost, W., Ethofer, T., Zentner, M., & Vuilleumier, P. (2012). Mapping Aesthetic Musical
Emotions in the Brain. Cerebral Cortex, 22(12), 2769–2783.
https://doi.org/10.1093/cercor/bhr353
Trost, W., Frühholz, S., Cochrane, T., Cojan, Y., & Vuilleumier, P. (2015). Temporal dynamics of
musical emotions examined through intersubject synchrony of brain activity. Social
Cognitive and Affective Neuroscience, 10(12), 1705–1721.
https://doi.org/10.1093/scan/nsv060
Turner, J. R., & Stanley, J. T. (2021). Holding on to pieces of the past: Daily reports of nostalgia
in a life-span sample. Emotion, 21(5), 951–961. https://doi.org/10.1037/emo0000980
Uddin, L. Q., Nomi, J. S., Hébert-Seropian, B., Ghaziri, J., & Boucher, O. (2017). Structure and
Function of the Human Insula. Journal of Clinical Neurophysiology, 34(4), 300.
https://doi.org/10.1097/WNP.0000000000000377
Umar Ismail, S., Cheston, R., Christopher, G., & Meyrick, J. (2020). Nostalgia as a
psychological resource for people with dementia: A systematic review and meta-analysis
of evidence of effectiveness from experimental studies. Dementia, 19(2), 330–351.
https://doi.org/10.1177/1471301218774909
U.S. Census Bureau. (2020). U.S. Census Bureau QuickFacts: Los Angeles city, California.
https://www.census.gov/quickfacts/fact/table/losangelescitycalifornia/PST045222#PST04
5222
Vess, M., Arndt, J., Routledge, C., Sedikides, C., & Wildschut, T. (2012). Nostalgia as a
Resource for the Self. Self and Identity, 11(3), 273–284.
https://doi.org/10.1080/15298868.2010.521452

276
Vincent, G. K. (2010). The next four decades: The older population in the United States : 2010 to
2050. U.S. Department of Commerce, Economics and Statistics Administration, U.S.
Census Bureau.
Vuong, V., Hewan, P., Perron, M., Thaut, M., & Alain, C. (2023). The neural bases of familiar
music listening in healthy individuals: An Activation Likelihood Estimation metaanalysis. Neuroscience and Biobehavioral Reviews, 105423.
https://doi.org/10.1016/j.neubiorev.2023.105423
Vuoskoski, J. K., Zickfeld, J. H., Alluri, V., Moorthigari, V., & Seibt, B. (2022). Feeling moved
by music: Investigating continuous ratings and acoustic correlates. PLOS ONE, 17(1),
e0261151. https://doi.org/10.1371/journal.pone.0261151
Wang, Y., Sedikides, C., Wildschut, T., Yang, Y., & Cai, H. (2023). Distress Prospectively
Predicts Higher Nostalgia, and Nostalgia Prospectively Predicts Lower Distress. Journal
of Personality. https://doi.org/10.1111/jopy.12824
Wang, Y., Wildschut, T., Sedikides, C., Wu, M., & Cai, H. (2023). Trajectory of Nostalgia in
Emerging Adulthood. Personality and Social Psychology Bulletin, 01461672221143241.
https://doi.org/10.1177/01461672221143241
Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures
of positive and negative affect: The PANAS scales. Journal of Personality and Social
Psychology, 54, 1063–1070. https://doi.org/10.1037/0022-3514.54.6.1063
Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York.
https://ggplot2.tidyverse.org
Wildschut, T., & Sedikides, C. (2022). Water From the Lake of Memory: The Regulatory Model
of Nostalgia. Current Directions in Psychological Science, 09637214221121768.
https://doi.org/10.1177/09637214221121768
Wildschut, T., & Sedikides, C. (2023). Water From the Lake of Memory: The Regulatory Model
of Nostalgia. Current Directions in Psychological Science, 32(1), 57–64.
https://doi.org/10.1177/09637214221121768
Wildschut, T., Sedikides, C., Arndt, J., & Routledge, C. (2006). Nostalgia: Content, Triggers,
Functions. Journal of Personality and Social Psychology, 91(5), 975–993.
https://doi.org/10.1037/0022-3514.91.5.975
Wildschut, T., Sedikides, C., & Kelley, N. J. (2023). Trait Nostalgia: Four Scales and a
Recommendation. Current Opinion in Psychology, 101608.
https://doi.org/10.1016/j.copsyc.2023.101608
Yang, Z., Izuma, K., & Cai, H. (2023). Nostalgia in the brain. Current Opinion in Psychology,
49, 101523. https://doi.org/10.1016/j.copsyc.2022.101523

277
Yang, Z., Wildschut, T., Izuma, K., Gu, R., Luo, Y. L. L., Cai, H., & Sedikides, C. (2022).
Patterns of Brain Activity Associated with Nostalgia: A Social-Cognitive Neuroscience
Perspective. Social Cognitive and Affective Neuroscience, nsac036.
https://doi.org/10.1093/scan/nsac036
Yinger, O. S., & Springer, D. G. (2022). Using Psycholinguistic Inquiry and Content Analysis to
Investigate Emotions in Memories of Musical Experiences. Musicae Scientiae, 26(2),
227–242. https://doi.org/10.1177/1029864920939603
Younes, L., Albert, M., Moghekar, A., Soldan, A., Pettigrew, C., & Miller, M. I. (2019).
Identifying Changepoints in Biomarkers During the Preclinical Phase of Alzheimer’s
Disease. Frontiers in Aging Neuroscience, 11. https://doi.org/10.3389/fnagi.2019.00074
Zaman, A., & Russell, C. (2022). Does autonoetic consciousness in episodic memory rely on
recall from a first-person perspective? Journal of Cognitive Psychology, 34(1), 9–23.
https://doi.org/10.1080/20445911.2021.1922419
Zator, K., & Katz, A. N. (2017). The language used in describing autobiographical memories
prompted by life period visually presented verbal cues, event-specific visually presented
verbal cues and short musical clips of popular music. Memory, 25(6), 831–844.
https://doi.org/10.1080/09658211.2016.1224353
Zhang, M., Yang, Z., Zhong, J., Zhang, Y., Lin, X., Cai, H., & Kong, Y. (2022). Thalamocortical
Mechanisms for Nostalgia-Induced Analgesia. Journal of Neuroscience, 42(14), 2963–
2972. https://doi.org/10.1523/JNEUROSCI.2123-21.2022
Zhang, X., Zhang, X., & Yu, X. (2023). Behavioral Intention of Repeated Watching and
Personality Traits: Testing Mediation Model of Nostalgia Arousal and Social
Connectedness. Psychology Research and Behavior Management, 16, 483–495.
https://doi.org/10.2147/PRBM.S391130
Zhang, Y., Brady, M., & Smith, S. (2001). Segmentation of brain MR images through a hidden
Markov random field model and the expectation-maximization algorithm. IEEE
Transactions on Medical Imaging, 20(1), 45–57. https://doi.org/10.1109/42.906424
Zhou, X., Sedikides, C., Mo, T., Li, W., Hong, E. K., & Wildschut, T. (2021). The Restorative
Power of Nostalgia: Thwarting Loneliness by Raising Happiness During the COVID-19
Pandemic. Social Psychological and Personality Science, 19485506211041830.
https://doi.org/10.1177/19485506211041830
Zhou, X., Sedikides, C., Wildschut, T., & Gao, D.-G. (2008). Counteracting Loneliness On the
Restorative Function of Nostalgia. Psychological Science, 19(10), 1023–1029.
https://doi.org/10.1111/j.1467-9280.2008.02194.x
Zinchenko, A. V. (2011). Nostalgia: Dialogue Between Memory and Knowing. Journal of
Russian & East European Psychology, 49(3), 84–97. https://doi.org/10.2753/RPO1061-
0405490306

278
Appendices
Appendix A: Supplementary Material for Chapter 1
Supplemental Methods
Control Song Selection Model
To identify “Control” songs that were musically matched to participant-selected
Nostalgia songs, we used Spotify’s web API (Lamere, n.d.), a free tool for Music Information
Retrieval (MIR) features that indexes any song from Spotify’s music library. Features available
through this tool include, among others, key, tempo, loudness, danceability, valence, and energy.
The Control song selection tool took in a user-inputted nostalgia-evoking song and
recommended a set of “candidate Control songs”, all of which were matched based on the
similarity of three key Spotify API features: valence, energy, and release date. Valence, defined
as a musical piece’s positiveness, was measured on a scale from 0.0 to 1.0, where 1 is highly
positive. Energy, defined as a song’s intensity or activity, was measured from 0.0 to 1.0. This
measure closely aligns with the concept of Arousal. Release date was additionally manipulated to
ensure that Nostalgia and Control song pairs would evoke familiarity from the same period of a
participant’s life and to Control for era-dependent musical style variations. We additionally set a
minimum threshold for popularity, a Spotify metric ranging from 0 to 100, to enhance the
likelihood that a song would be rated as “familiar” to a given participant. Our previous work
indicated that manipulating these key features was sufficient to provide songs that were matched
for all other available Spotify API features (Greer, et al., under review)
We used Spotify's Recommendations call to compile a list of four candidate Control
songs for every Nostalgia song, in which the minimum popularity was 0.80, the valence and

279
energy were matched to the Nostalgia song within 0.15 points, and the release date within 5
years of that of the Nostalgia song. In the case that Spotify could not generate recommendations
for an input song, a respondent was prompted to enter another nostalgic song.
Control Song Identification
After inputting three nostalgia-inducing songs, the Control song selection tool internally
generated 12 control song candidates (4 for each nostalgia-inducing song). This process took less
than 1 second and was supported by a continuously-running JavaScript program that linked the
SpotifyAPI program to the back-end of the Qualtrics survey in real time. This script and a webbased version of this tool is publicly available at [link available upon acceptance] and [webpage
link available upon acceptance]. Participants were then presented a 30-second clip from a
maximum of 15 songs in random order: the three songs that they input as nostalgic, and up to
four songs that were recommendations for each nostalgic song presented in a set, which we will
call “candidate Control songs”. The 30-second clip was chosen by Spotify’s automatic preview,
which is likely chosen by a music segmentation algorithm. After each song, participants rated
their familiarity with the song (“Not at all familiar,” “Somewhat familiar,” “Very familiar”). If
songs were rated as “Somewhat familiar” or “Very familiar”, participants rated how nostalgic the
song made them feel on a scale from 1 (“Not nostalgic at all”) to 9 (“Extremely nostalgic”).
Under the condition that a subject labeled a candidate Control song as sufficiently familiar
(“Somewhat” or “Very”) and sufficiently not nostalgic (<5 on the nostalgia rating scale), the
other candidate Control songs from the set would not be presented to that subject. In the case that
a song was either too nostalgic or not familiar, participants would continue to listen to the next
Control song candidate (see Appendix A: Figure S1).
Comprehension Check Questions

280
Participants were asked to complete a comprehension check, in which they were asked to
choose the definition of “nostalgia” and of a “nostalgia-evoking song”, “as this study defines it”
from a list of five options. Options for “nostalgia” included: 1. "a sentimental memory of the
past", 2. "a reminder of something familiar from the past", 3. "thinking of the past", 4. "a
reminder of the good old days", 5. "a sentimental longing for the past". Options for “nostalgiaevoking song” included: “1. A song that brings you back to a pleasant era in your life", 2. "A
song that evokes a strong memory" , 3. "A song that brings you back to a pleasant moment in
your life AND makes you feel sad" , 4. "A song from the past that reminds you of the good old
days AND makes you feel happy" , 5. "A song that brings you back to a pleasant moment or era
in your life AND evokes a strong memory". Option order was randomly shuffled for each
participant.

281
Figure S1
Control Song Selection Procedure.

282
Figure S2.
Top 50 genres in the Nostalgic and Control songs.
Note. Genre was obtained through Spotify’s API, which assigns a genre label to an artist, rather
than a track. Therefore, multiple genres are supplied for each artist; here, the top genre for each
artist was obtained and frequencies of genres counted across all songs within each condition.
Size indicates greater frequency.

283
Figure S3
Correlations between and within TIPI and BANPS.

284
Table S1.
Means and standard deviations (SD) of computer-derived musical features for Nostalgia and
Control Songs.
Nostalgia Control
Feature Mean SD Mean SD
Acousticness 0.23 -0.28 0.21 -0.27
Danceability 0.58 -0.16 0.67 -0.21
Energy 0.66 -0.21 0.67 -0.21
Instrumentalness 0.05 -0.16 0.05 -0.17
Liveness 0.18 -0.15 0.19 -0.15
Loudness (dB) -7.51 -3.64 -7.33 -3.48
Popularity 62.42 -17.49 63.28 -17
Release Year 2000.61 -15.21 2000.62 -15.3
Speechiness 0.07 -0.07 0.07 -0.07
Tempo (BPM) 121.2 -29.26 120.97 -27.77
Valence 0.52 (0.24) 0.56 (0.24)
Note. Acousticness, danceability, instrumentalness, liveness, and speechiness are on a scale of 0
to 1, in which a score closer to one denotes more intensity of that feature. Valence and energy are
also on a scale of 0 to 1; however, 0.5 indicates the “neutral” midpoint, 1 is high or positive, and
0 is low or negative. Popularity is on a scale of 0 to 100, where higher values indicate more
streams on Spotify.

285
Appendix B: Supplementary Material for Chapter 2
Supplementary Tables
Table S1.
Multi-level model results for higher-level LIWC categories.
variable estimate t_value p_value _2 _00 Marginal
R2 /
Conditional
R2
Drives 3.80 32.50 1.11E-142 16.95 4.11 id 0.000 /
0.195
Cognition 12.55 73.53 0 33.23 9.73 id 0.000 /
0.226
Affect 6.84 44.82 5.73E-211 23.21 8.97 id 0.000 /
0.279
Social 7.44 45.78 6.91E-217 32.32 8.05 id 0.000 /
0.199
Culture 0.39 13.04 7.07E-35 1.55 0.12 id 0.000 /
0.070
Lifestyle 8.04 50.32 2.44E-236 36.26 6.04 id 0.000 /
0.143
Physical 0.73 16.36 4.70E-51 3.93 0.08 id 0.000 /
0.020
Perception 13.80 82.43 0 47.26 4.02 id 0.000 /
0.078
Note. One-sample tests were performed.

286
Table S2.
Multi-level model results for lower-level LIWC dictionary words.
variable coefficient t_value p_value σ2 τ00 Marginal
R2 /
Conditiona
l R2
i 10.14 74.13 0 18.07 7.41 id 0.000 /
0.291
affiliation 3.10 27.82 4.27E-116 13.46 4.42 id 0.000 /
0.247
insight 3.36 34.51 6.15E-156 9.9 3.48 id 0.000 /
0.260
cause 1.99 28.30 3.62E-120 5.82 1.59 id 0.000 /
0.214
differ 1.36 24.28 4.15E-96 4.83 0.60 id 0.000 /
0.110
memory 3.55 36.07 9.62E-164 9.43 3.82 id 0.000 /
0.288
tone_pos 5.36 40.12 3.54E-185 18.67 6.58 id 0.000 /
0.261
emo_pos 3.02 28.74 1.86E-121 11.02 4.25 id 0.000 /
0.279
socbehav 2.25 28.44 1.09E-119 10.25 1.00 id 0.000 /
0.089
socrefs 4.76 38.37 5.61E-177 20.01 4.29 id 0.000 /
0.176
leisure 5.16 41.21 7.25E-192 24.05 3.09 id 0.000 /
0.114
work 2.11 21.65 8.43E-81 13.41 2.24 id 0.000 /
0.143
allure 7.07 52.21 7.30E-247 24.55 4.86 id 0.000 /
0.165
motion 1.99 26.52 6.38E-109 8.18 1.26 id 0.000 /
0.134
space 5.54 42.03 5.56E-192 24.98 4.00 id 0.000 /
0.138
auditory 4.10 38.86 5.02E-178 12.29 3.85 id 0.000 /
0.238
feeling 1.68 23.32 4.37E-90 4.74 2.13 id 0.000 /
0.311
time 5.74 47.62 3.05E-222 22.51 2.78 id 0.000 /
0.110
focuspast 6.88 52.82 5.47E-252 16.87 6.54 id 0.000 /
0.279

287
focuspresen
t
2.79 30.78 1.87E-134 10.17 2.48 id 0.000 /
0.196
Note. One-sample tests were performed.

288
Appendix C: Supplementary Material for Chapter 3
Supplemental Methods
Comprehension check questions for online survey
Participants were asked to complete a comprehension check, in which they were asked to choose
the definition of “nostalgia” and of a “nostalgia-evoking song”, “as this study defines it” from a
list of five options. The options, with order shuffled randomly, for “nostalgia” were: 1) "a
sentimental longing for the past", 2) "a sentimental memory of the past", 3) "a reminder of
something familiar from the past", 4) "thinking of the past", 5) "a reminder of the good old
days". The options, with order shuffled randomly, for a “nostalgic song” were: 1) "A song that
brings you back to a pleasant era in your life", 2) "A song that brings you back to a pleasant
moment or era in your life AND evokes a strong memory", 3) "A song that evokes a strong
memory", 4) "A song that brings you back to a pleasant moment in your life AND makes you
feel sad", 5) "A song from the past that reminds you of the good old days AND makes you feel
happy".

289
Supplemental Tables
Table S1.
Results of multilevel models for subjective appraisals of nostalgia, control, and unfamiliar songs
between age groups.
Multilevel Regression Model Results for Subjective Song Appraisals.
Nostalgi
a Rating
Positive
Valence
Negative
Valence
High
Arousal
Low
Arousal
(Intercept) 2.662**
*
3.537**
*
1.547**
*
2.870**
*
2.282**
*
(0.130) (0.342) (0.296) (0.362) (0.341)
conditionNostalgia 5.754**
*
4.605**
* 0.005 3.144**
* 0.798
(0.158) (0.332) (0.339) (0.359) (0.432)
agegroupolder -0.076 1.324* 0.108 1.446** -0.109
(0.187) (0.486) (0.421) (0.514) (0.485)
conditionNostalgia
× agegroupolder 0.253 -0.375 -0.588 0.023 -0.389
(0.226) (0.473) (0.483) (0.513) (0.616)
conditionControl 0.105 -0.676* -0.139 -0.599
(0.281) (0.225) (0.289) (0.299)
conditionControl ×
agegroupolder -0.359 0.256 -1.162* 0.461
(0.401) (0.321) (0.412) (0.426)
Cor
(Intercept~conditio
nNostalgia id)
-0.865 -0.844 -0.658 -0.392 -0.503
Cor
(Intercept~conditio
nControl id)
-0.358 -0.853 -0.478 -0.886

290
Nostalgi
a Rating
Positive
Valence
Negative
Valence
High
Arousal
Low
Arousal
Cor
(conditionControl~c
onditionNostalgia
id)
0.450 0.758 0.614 0.846
R2 Marg. 0.885 0.442 0.018 0.291 0.026
R2 Cond. 0.908 0.612 0.328 0.522 0.287
* padjusted < 0.05, ** padjusted < 0.01, *** padjusted < 0.001

291
Table S2.
Results of multilevel models for spotify features between song conditions.
Multilevel Regression Model Results for Spotify Features
SSA Valence energy
Release
year
popularit
y
danceabi
lity loudness speechin
ess tempo instrume
ntalness
acoustic
ness
liveness
(Intercep
t)
16.861*
**
0.516**
*
0.547**
*
1995.20
4***
40.149*
**
0.561**
*
-
10.000*
**
0.058**
*
119.095
***
0.090**
*
0.365**
*
0.178**
*
(1.78) (0.017) (0.020) (2.285) (1.548) (0.013) (0.502) (0.004) (1.753) (0.020) (0.026) (0.009)
conditio
nControl -0.116 0.020 0.008 -0.102 8.060* 0.000 0.482 0.004 -0.457 -0.014 -0.011 0.000
(0.995) (0.019) (0.017) (0.995) (1.570) (0.012) (0.339) (0.005) (2.257) (0.016) (0.022) (0.012)
conditio
nNostalg
ia
-0.447 -0.020 0.003 -0.450 17.995* -0.014 0.312 0.001 0.638 0.011 0.023 0.000
(0.994) (0.019) (0.017) (0.995) (1.568) (0.012) (0.339) (0.005) (2.256) (0.016) (0.022) (0.012)
R2
Marg. 0.000 0.004 0.000 0.000 0.105 0.002 0.001 0.001 0.000 0.002 0.002 0.000
R2
Cond. 0.487 0.114 0.251 0.627 0.232 0.185 0.374 0.108 0.035 0.304 0.238 0.014
* padjusted < 0.05, ** padjusted < 0.01, *** padjusted < 0.001

1

Abstract (if available)

Abstract Nostalgia, a sentimental longing for the past, is a pancultural emotion experienced by individuals across the lifespan. Music, particularly from adolescence and early adulthood, is a potent nostalgia trigger. While previous research has documented nostalgia’s affective characteristics and psychological functions, less is known about nostalgia as evoked by music, and very little is known about nostalgia in older adults. Specifically, the affective profile, content, and neural basis of music-evoked nostalgia across the lifespan are poorly understood. This dissertation encompasses four chapters, with data collected from 805 participants, examining this concept. In this work, I show that music-evoked nostalgia is a mixed, albeit primarily positive, emotion that contains a delicate interplay of affect and personality (Chapter 1). I demonstrate that nostalgic music is derived mainly from the teenage and early adulthood years of both the individual and their primary childhood caretakers. I show evidence of social cross-generational transfer and that the content of music-evoked nostalgic experiences is mainly positive, social, and self-salient (Chapter 2). Finally, I demonstrate that music-evoked nostalgia is accompanied by widespread neural activation and co-activation in the default mode network, reward regions, and medial temporal lobe (Chapter 3) in younger and older adults. Lastly, I show that these neural regions are recruited synchronously across older adult participants while listening to a full-length nostalgic piece of music (Chapter 4). In sum, this dissertation contributes to the psychological and neural understanding of music-evoked nostalgia across the lifespan.

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Core Title The behavioral and neural correlates of music-evoked nostalgia across the lifespan

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Degree Conferral Date 2024-08

Publication Date 12/20/2024

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