Putting Genetics in Aesthetics: Etiological Sources of Variation in Aesthetic Chills (Thesis)

Abstract

Aesthetic chills, broadly defined as a marker of bodily mixed emotional-hedonic responses from aesthetic phenomena, are universally shared yet diversified phenomena that encompass every human culture. However, while individual differences in aesthetic chills are related to behavioral, personality, functional and structural brain individual differences, no study to date has explored the genetic etiological sources of variation of such a trait. To partition genetic and environmental sources of variation, we conduct a biometric model fitting on data from 8733 twin pairs, collected over the arch of 9 years by the Netherlands Twin Register. Both genetic and environmental factors accounted for by the individual variance in aesthetic chills. We thus outlined for the first time, that intense aesthetic experiences, as one of feeling aesthetic chills, are not shaped by nurture alone, but they also obey to the underlying genetic propensity that defines our biological self.

Full text

Running head: PUTTING GENETICS IN AESTHETICS
Putting Genetics in Aesthetics: Etiological Sources of Variation in Aesthetic Chills
Student ID1: 33556462
Supervisor1: Rebecca Chamberlain
Cosupervisor2: Dorret I Boomsma
30/08/2019
1Department of Psychology, Goldsmiths University of London
2Department of Biological Psychology, VU University Amsterdam, The Netherlands
Thesis submitted as partial fulfillment of the MSc program in Psychology of the Arts,
Neuroaesthetics, and Creativity

PUTTING GENETICS IN AESTHETICS
2
Table of contents
- Acknowledgment
- Inclusion of statement on shared data collection
0. Abstract
1. Introduction
1.1 Overview
1.2 Aesthetic Chill: what they are and what they portend to be
1.3 Neuro-physio-biological correlates of aesthetic chills
1.4 Missing heritability: what we know about genomic and aesthetics
1.5 Aim
2. Methods
2.1 Participants
2.2 Materials
2.3 Procedure
2.4 Genetic analysis
2.5 Biometric modelling
3. Results
3.1 Descriptive
3.2 Biometric modeling
4. Discussion
4.1 Genetic sources of variation in aesthetic chills
4.2 Environmental source of variation in aesthetic chills
4.3 Sex differences
4.4 Limitation & further research
4.5 Conclusion

PUTTING GENETICS IN AESTHETICS
3
Acknowledgment
First and foremost, I would like to acknowledge the Netherlands Twin Register and the twins
who took part in the longitudinal inquire on the genetic and environmental architecture
underlying the differences that make us what we are. Without the immense work conducted
over more than 30 years, small research such as the one we conducted here would not have
been possible. From the NTR a special thanks goes to: Zenab Tamimy, for helping me with
the theoretical background on behavioural genetics and path analysis, Dorret I Boomsma, for
the co-supervision of the work, the possibility of using the database and for her several
insightful recomendations; And to Sofieke Kevennar, who patiently spent most of her time
teaching me the fundamentals of OpenMx, even when she was supposed to prepare for one of
the most important conferences of the year in behavioural genetics.
From Amsterdam to London, I would like to thank the entire MSc cohort program
Psychology of the Arts, Neuroaesthetics, and Creativity (PANC). It has been a stressful but
wonderful year. Thank you, I would not be here if not for you. In particular, a special thanks
go to Kirren Chana, for helping me and my sometimes-weird English. Further, I would like to
extend my special thanks to my supervisor, Rebecca Chamberlain, who believed in the
project since the beginning.
From London to Canada, I would like to acknowledge MacKenzie Trupp, for the insightful
ideas and conversation on bodily reaction to aesthetic experiences, for her fundamental help
with the writing process, and for being always keen to listen and discuss. Thanks Mac.
Moreover, a special thanks goes to Madi Trupp, who proofread the last version of the
manuscript.
Finally. From Canada to Italy I would like to thank my family who supported me (both
morally and economically). A special thanks go to my Grandma, who sponsored my MSc.
This thesis is for her. Even if she cannot read it in English.

PUTTING GENETICS IN AESTHETICS
4
Inclusion of statement on shared data collection
The data used in this study were collected by the Netherlands Twin Register (NTR, see:
www.tweelingenregister.org), a longitudinal cohort established in 1987 by the department of
biological psychology at the Vrije Universiteit of Amsterdam, registered with the Dutch Data
Protection Authority (nr m1412317). The procedure for the formal request of phenotypic data
was carried out after the approval of the ethical committee, and following the guidelines
provided by the NTR.
The collection of the data as used in this study occurred from the year 2004 till the year 2013
(Ligthart et al., in press).

PUTTING GENETICS IN AESTHETICS
5
Abstract
Aesthetic chills, broadly defined as a marker of bodily mixed emotional-hedonic responses
from aesthetic phenomena, are universally shared yet diversified phenomena that encompass
every human culture. However, while individual differences in aesthetic chills are related to
behavioural, personality, functional and structural brain individual differences, no study to
date has explored the genetic etiological sources of variation of such a trait. To partition
genetic and environmental sources of variation, we conduct a biometric model fitting on data
from 8733 twin pairs, collected over the arch of 9 years by the Netherlands Twin Register.
Both genetic and environmental factors accounted for by the individual variance in aesthetic
chills. We thus outlined for the first time, that intense aesthetic experience, as one of feeling
aesthetic chills, are not shaped by nurture alone, but they also obey to the underlying genetic
propensity that define our biological self.

PUTTING GENETICS IN AESTHETICS
6
‘Separated by geographic and economic continents,
when two brothers, estranged at birth,
were brought to tears
by the same Chopin nocturne ...,
they seemed to be responding
to some subtle, common chord
struck by their genomes’
(Mukherjee, 2016:382)
‘In order to bask in that magic
a wise reader reads the book of genius
not with his heart,
not so much with his brain,
but with his spine’
(Nabokov, 2017:6)

PUTTING GENETICS IN AESTHETICS
7
Introduction
1.1 Overview
Aesthetic experiences, which can be defined as affective meaningful evaluation
1
(Pearce et
al., 2016), are universally shared and uniquely diversified (Nadal & Chatterjee, 2019)
phenomena that shape the way humans interact with the world they inhabit. They encompass
emotional-hedonic and sensory-motor evaluations (Chatterjee & Vartanian, 2014), as well a
social-bonding practices (Dissanayake, 2001) and cognitive functioning (Fancourt & Steptoe,
2018). However, while every individual in every observed culture feels such experiences,
not everyone feels them in the same way (Nadal & Chatterjee, 2019). Psychological,
anthropological, sociological, and recently neuroscientific research, all embraced, even by
employing different methodological approaches, a quest to describe such experiences.
Specifically, aesthetic science, a scientific field as old as the beginning of psychological
sciences (Fechner, 1876), tried to measure, objectify and compare what is subjectively
perceived as beautiful (Kawabata & Zeki, 2004), moving (Vessel, Starr, & Rubin, 2013),
pleasurable (Blood & Zatorre, 2001), or more generally of emotional value. One such
experience that has been reliably capture over the course of three decades is the aesthetic
chill. In this section we will review behavioural, physiological, neurobiological and
personality factors that have been associated with such phenomenon. Finally, we will
introduce the aim of this study. Namely to fill a gap in the literature of aesthetic science
through the analysis of individual differences in the propensity of feeling aesthetic chills by
mean of genomic studies. By doing so, we will show that aesthetic experiences, and
specifically aesthetic chills, are bodily experiences and that as such, they also obey the rules
that define our biological self.
1.2 Aesthetic Chill: what they are and what they portend to be
Since the seminal work of Goldstein (1980) aesthetic chills, less often called thrills
(Goldstein, 1980), shivers (Sloboda, 1991), frisson (Huron, 2006) or skin orgasm (Harrison &
Loui, 2014) consistently showed up as a reliable tool to access and measure mixed emotional
hedonic responses from a variety of aesthetic phenomena, such as music (Grewe, Kopiez, &
Altenmüüller, 2009), poetry (Wassiliwizky, Koelsch, Wagner, Jacobsen, & Menninghaus,
1
Aesthetics, deriving from the Greek word aisthetikos, literally means through the senses. However, it is
important to note that the definition given here is one among other disputable definition

PUTTING GENETICS IN AESTHETICS
8
2017), film clips (Wassiliwizky, Jacobsen, Heinrich, Schneiderbauer, & Menninghaus, 2017),
visual images and nature (Silvia, Fayn, Nusbaum, & Beaty, 2015), and even eloquent
speeches (Schurtz et al., 2012).
Firstly, aesthetic chills are frequently self-reported by individuals as moments of
intense emotional experiences (Blood & Zatorre, 2001; Goldstein, 1980; Panksepp, 1995).
For instance, a variety of quantitative analysis of self-reported feelings found aesthetic chills
to match moments of emotional peaks and liking (Grewe et al., 2009; Rickard, 2004;
Salimpoor, Benovoy, Larcher, Dagher, & Zatorre, 2011). These resemblances endured
physiological testing, since subjective reports of the intensity of such feelings are highly
synchronized with physiological measurement of chills (see table 1 for a review of
physiological responses of aesthetic chills). Moreover, Bannister’s (2018) thematic analysis
on a sample of 375 individuals, showed that the underlying theme on a variety of chills
related questions are linked to emotions and feelings, thus showing that those findings are
going beyond quantitative results. It is also worth noting that such shreds of evidence are
peculiar to emotional responses and not just of arousal, as suggested by Rickard (2004), who
found bodily chill responses to emotional stimuli to differ from the one evoked by arousing
stimuli.
Interestingly, while aesthetic chills have been linked to emotional reactions,
confirming the possibility of using chills as a marker of peak emotional responses, there is
still no consensus on the valence of such emotions. That is, the valence of emotional reaction
linked to chill experiences is mixed and/or idiosyncratic (Mori & Iwanaga, 2017;
Wassiliwizky, Wagner, Jacobsen, & Menninghaus, 2015). For example, Goldstein (1980)
reported happy eliciting music to be more prone to cause chills, while Panksepp (1995),
found that sad eliciting music was more likely to evoke chills than joy eliciting music.
Surpassing behavioural self-report, Wassiliwizky et al. (2017) found that the activity of the
corrugator supercilii, a facial muscle indicator of emotional sadness, was higher in
concomitance of the experience of aesthetic (pleasurable) chills, adding further evidence in
favour of pleasurable sadness as a mixed emotional state involved during the experience of
chills. Besides happiness and sadness, Huron (2006) theorized that aesthetic chills, derived
from resolved initially violated expectations of some structural musical properties, were more
related to the re-evaluation of fight or flight emotional responses (such as fear), in a safer
aesthetic context. Furthermore, besides basic emotional states, intense unusual emotions were
also reported in the literature. For example, in his thematic analysis Bannister (2018) reported
aesthetic chill to be related with mixed valenced emotional states, such as being moved, the

PUTTING GENETICS IN AESTHETICS
9
sensation of awe and of feeling touched. Further, findings on unusual emotional states in
aesthetic experiences posit the chill factor, a combination of questions on the propensity and
the intensity of bodily responses from aesthetic phenomena in the self-reported Aesthetic
Emotion Scale (AES, Silvia & Nusbaum, 2011), as to be highly correlated with the factor
absorption and being touched. Resemblance that has been replicated in the work of
Wassiliwizky et al. (2015), who found the likelihood of getting chills, measured by
physiological markers, to be increased as a function of the intensity of the reported emotional
response of being moved.
It is worth noting that while aesthetic chills might be evoked either by positively,
negatively, or mixed valenced emotional states, they are frequently associated with positive
hedonic responses
2
(Salimpoor et al., 2011). Indeed, a variety of studies published on such
experiences have assumed aesthetic chills as highly pleasurable, and peak-alike phenomena,
as exemplified by the term ‘skin orgasm’ used by some (Panksepp, 1995). In addition, the
term chills has also been used as the top rating option of Likert scale designed to capture
pleasurable experiences (Mas-Herrero, Zatorre, Rodriguez-Fornells, & Marco-Pallarés,
2014). Finally, further evidence are coming from the findings on the overlap between the
neurobiological correlates of such experiences and the ones that account for by pleasurable
rewarding experiences (Blood & Zatorre, 2001; Salimpoor et al., 2011).
Additionally, besides being related to emotional and pleasurable responses, aesthetic
chills are also strongly embodied phenomena (Bannister, 2018; Grewe et al., 2009; Sachs,
Ellis, Schlaug, & Loui, 2016; Wassiliwizky, Koelsch, et al., 2017). Participants that report the
experience of chills more frequently report sidewise sensation of thrills in the upper back of
the neck, spine, and back (Goldstein, 1980), shivers down the spine (Sloboda, 1991), tingling
sensations in the arm (Craig, 2005), and more generally diffuse bodily reactions. Bodily
responses that are associated with more abstract emotion, as showed by Sachs (2016)
multidimensional analysis of the aesthetic experience scale (AES) in a sample of 237
participants, which positioned the experience of chills in the middle between visceral (i.e
heart-racing, pit of stomach, lump in throat,…) and abstract aesthetic emotions (i.e, absorbed,
touched, awe,…). Finally, piloerection (or goosebumps), the autonomous response resulting
in hair standing on end, is another physical manifestation of chills. However, it is worth
2
In this regard, it is important to make a distinction between aesthetic chills, and aversive related chills, which are usually
related with feelings of separation, loss, fear, disgust, and thus unpleasurable (Maruskin, Thrash, & Elliot, 2012;
Wassiliwizky, Wagner, Jacobsen, & Menninghaus, 2015).

PUTTING GENETICS IN AESTHETICS
10
noting that chills are a necessary but not sufficient condition for piloerection to occur
(Wassiliwizky, Koelsch, et al., 2017). Indeed, the percentage of objective
piloerection accompanied by self-report of chills is roughly 40% for poetry (Wassiliwizky,
Koelsch, et al., 2017), film clips (Wassiliwizky, Jacobsen, et al., 2017), soundtracks
(Benedek & Kaernbach, 2011), and music (Sumpf, Jentschke, & Koelsch, 2015)
All in all, aesthetic chills pretend to be a correlate of peak pleasurable bodily
emotional reactions to aesthetic phenomena. Importantly, such findings go beyond self-
reports alone, encompassing consistently objectifiable physiological correlates.
1.3Neuro-physio-biological correlates of aesthetic chills
As shown in table 1, Aesthetic chills have been associated with concurrent dynamic
peripheral changes mediated by the Sympathetic Autonomous Nervous System (SNS).
Peripheral dynamic changes that occur before and after chill onset, which is defined as the
moment in time when participants report chill experiences. For example Wassiliwizky
(2017), reported an increase of phasic Electrodermal Activity (pEDA) event-related grand
average for moments of chills (measured as moments of concurrent self-report of chills and
piloerection) evoked by poetry, in comparison with moments of no chills, after and before the
chill onset (see figure 1).
Figure 1 Examples of skin conductance related-measures changes over time during moments of aesthetic chills.
a. SCR for 10 sec before and after the chill onset compared with a random 20 s excerpt (Grewe et al., 2009) b.
Comparison of normalized SCR in individual who can experience pleasure from music and individuals who
cannot (red) (Mas-Herrero et al., 2014); c. Event related grand average before and after the chill onset
(Wassiliwizky, Koelsch, et al., 2017).
Among all the physiological changes associated with aesthetic chills the most
consistent is the steady increase of skin conductivity, as measured by Skin Conductance
Responsivity (SCR), Galvanic Skin Response (GSR), Skin Conductance Level (SCL) or

PUTTING GENETICS IN AESTHETICS
11
pEDA. Measurements that indicate sweat glands activation, which is an established measure
for nervous system arousal and general emotional responses (Kreibig, 2010).
However, while aesthetic chill and pleasing and arousing experiences share similar
physiological correlates, such as SCR increase, chills are unique in the sense that are a signal
of peak-alike intensity. For instance, Mass-Herrero (2014) found that SCR was higher during
moment of chills then moment of liking, adding evidence toward the peak nature of such
experiences, which has been proposed to be one of the distinct features of chills. Indeed,
Grewe (2009) showed that moments of chills evoked by music, measured by GSR, correlate
with the peak of self-report valence and feelings. This led him to conclude that chills are the
combination of intense subjective embodied feelings of pleasure arising from emotional, and
thus should be thought of as a marker of peak emotional experiences.
Besides SNS changes reported in Table 1, other physiological correlates have been
reported in the literature. Sumpf et al. (2015) found the Ek ratio of cardiac amplitude, which
is associated with the propensity of experience positive emotion, to be significantly higher
than baseline right before and after moments of piloerection, thus indicating a correlation
between variation of regional cardiac activity and aesthetic chills. Finally, Laeng et al. (2016)
showed increased eye pupil diameter, a marker of ANS arousal, in concomitance of chill
experiences.
Further evidence on bodily changes occurring during and in anticipation of aesthetic
chills are coming from neuroimaging studies. Blood and Zatorre’s (2001) Positron Emission
Tomography (PET) study during moment of chills identified stronger activity of areas
including the anterior Cingulate Cortex (CC) and the bilateral Insula (Ins), which are thought
to play a role in visceral and neural representations of inner body states (Craig, 2005),
aesthetic experiences (Brown, Gao, Tisdelle, Eickhoff, & Liotti, 2011), and more generally to
signalling elements in the environment that are pertinent to the organism needs (Skov, 2019).
Interestingly, when controlled for physiological effects, the parametric activation of
the Ins lost its significance, suggesting a link between physiological activity related to chills
and Ins activation. Similar results came from the study of Sachs et al. (2016), who found the
chill factor score, extracted from the AES for music, to be associated with tract volume
differences connecting the posterior Superior Temporal Gyrus (pSTG), with the anterior Ins
and medial PreFrontal Cortex (PFC). That is, the more the participants reported and
experienced chills from music, the larger the volume of white matter tract between sensory
specific areas and evaluative-salience areas. Further, a recent study on one patient who
suffered a lateralized tract lesion between the left a-Ins and left PFC showed inability of

PUTTING GENETICS IN AESTHETICS
12
producing any SCR variation during moment of music induced chills (Grunkina et al., 2017),
leading the authors to suggest that salience is a pivotal mechanism for the embodiment of the
aesthetic chill.
Besides chills induced by music, two other studies confirmed the role of the Ins and
the CC on the experience of chills. The first by Wassiliwizky (2017), which conducted an f-
MRI study on the neural correlates of poetry-evoked chills, and the second by Williams
(2018), who used resting-state brain data to compare functional connectivity on individuals
who reported higher propensity to feeling chills. Both authors found the Ins and the CC to be
involved in the experience of chills, with the first finding the Mid-CC and the posterior-Ins to
be more active after the chill onset evoked by poetry, and the left anterior Ins and ACC to be
more active before it. The second author found participants who reported higher propensity
of feeling chills (Item 188 of NEO-PI-R, see further for more information) to have enhanced
connectivity between the network composed by the ins and the CC (salience network) and a
network composed by the other from the medial Prefrontal Cortex (PFC) and
Parahippocampal regions.
Finally, Besides bodily related brain areas, another consistent pattern of results
anchored chills to neurobiophysiological correlates of general rewards (Blood & Zatorre,
2001; Sachs et al., 2016; Salimpoor et al., 2011), and to sensory specific regions (Loui et al.,
2017; Martínez-Molina, Mas-Herrero, Rodríguez-Fornells, Zatorre, & Marco-Pallarés, 2019;
Sachs et al., 2016). Indeed the Nucleus Accumbens (NAcc), a crucial region for reward
processing (Salimpoor et al., 2011; Wassiliwizky, Koelsch, et al., 2017) , deputed to process
and compare expected value with perceived value (Gold et al., 2019), has consistently been
linked to the experience of chills, before (Salimpoor et al., 2011), and after (Wassiliwizky,
Koelsch, et al., 2017) its onset. Further, preliminary studies (Goldstein, 1980) showed that
naltrexone, a drug that works as antagonist of mu-opiod receptors, and thus decrease the
overall pleasure experiences, decrease the frequency of getting chills. Moreover white matter
tract between the NAcc and the pSTG structural differences have been linked with decreased
likelihood of getting chills from music (Loui et al., 2017; Sachs et al., 2016).
All in all, this studies accumulated evidence towards the involvement of important
neural correlates of bodily saliency, reward and sensory regions, and heightened connectivity
between such regions on the experience and the anticipation of chills.

PUTTING GENETICS IN AESTHETICS
13
Table 1
Review of the physiological correlates of chill
Authors
SC
ST
HR
RR
Stimuli
N
(Blood & Zatorre, 2001)
no
no
↑
↑
music
10
(Rickard, 2004)
↑
no
no
-
music, video
21
(Craig, 2005)
↑
no
-
-
music
32
(Guhn et al., 2007)
↑
-
no
-
music
27
(Grewe et al., 2009)
↑
-
↑
-
music
95
(Grewe et al., 2011)
↑
-
↑
no
music, picture, sounds
36
(Benedek & Kaernbach, 2011)
↑
-
no
↑
music, audio
50
(Salimpoor et al., 2011)
↑
↓
↑
-
music
10
(Mas-Herrero et al., 2014)
↑
-
↑
-
music
30
(Sumpf et al., 2015)
-
-
↑
no
music
58
(Sachs et al., 2016)
↑
-
↑∗
-
music
20
(Colver & El-Alayli, 2016)
↑
-
-
-
music
113
(Mori & Iwanaga, 2017)
↑
-
no
no
music
66
(Wassiliwizky et al., 2017a)
↑
-
no
-
poetry
27
(Wassiliwizky et al., 2017b)
↑
-
↑
-
movie
25
Table 1 Sympathetic Nervous System activity associated with aesthetic chills. Skin Conductance (SC) refers to a
variety of electrodermal related activity measurements; Skin Temperature (ST); Heart Rate (HR); Respiratory
Rate (RR); number of individuals participating in the experiment (N); represents increase activity, represents
decreased activity. Some measurement, such as eye pupil dilatation, volume pulse amplitude or respiratory
depth has not been reported due to the scarcity of studies using them. *reported Inter Beat Interval, which is a
reverse measurement of HR.
1.4 missing heritability: what we know about genomic and aesthetics
While the outlined growing body of research accumulated behavioural, physiological
and neurobiological evidence on the nature of subjective bodily experience of aesthetic chills,
genomic studies are yet missing. Such lack of applications of genomic methodologies is not
unique to aesthetic chills alone but shared among the entire field of aesthetic science. Indeed,
while speculations on innate predisposition of aesthetic sensitivity (see Che, Sun, Gallardo, &
Nadal, 2018) and theoretical works on the common biological mechanism that underlie the
universality of aesthetic experiences (Nadal & Chatterjee, 2019; Westphal-Fitch & Fitch,
2018) have been put forward, the field of aesthetic science is still lacking empirical

PUTTING GENETICS IN AESTHETICS
14
investigation on the etiological sources of variation underlying individual differences in such
experiences.
This is rather surprising when considering the results obtained in the past decades by
genetic studies on personality and attitudinal individual differences. Indeed, estimates for
heritability (i.e. the percentage of variance of a given trait that can be explained by genetic
factors, see method section) for Openness to Experiences (OE), which is an important
predictor of individual differences in aesthetic experiences (Chamorro-Premuzic, Reimers,
Hsu, & Ahmetoglu, 2009; Fayn, MacCann, Tiliopoulos, & Silvia, 2015; Nusbaum & Silvia,
2013), is the highest among the big five personality factors (McCrae, Kurtz, Yamagata, &
Terracciano, 2011). For example, Bouchard’s (2003) review of four large twin sample studies
conducted in Canada, USA and Germany, reported that 57% of the combined samples’
variance in individual differences on OE to be accounted for by genetic factors.
Remarkably, Aesthetic Sensitivity, one of the 6 sub-factors loading on OE (Fantasy,
Feelings, Action, Ideas, Values and Aesthetic Sensitivity) which describes the individual
tendencies to be 'moved by poetry, absorbed in music, and intrigued by art’(Costa & McCrae,
2008), is relatively highly heritable (McCrae et al., 2011). Heritability estimates for the
Aesthetic Sensitivity sub factor have consistently been reported as one of the highest among
all the 30 sub facets of the NEO-PI-R (Jang, 1996), across different cultures (Jang, Livesley,
& Vemon, 1996; Jang, McCrae, Angleitner, Riemann, & Livesley, 1998; McCrae,
Terracciano, & 78 Members of the Personality Profiles of Cultures Project, 2005). For
example, McCrae (2011) analysis on a combined sample of Canadian (N =900), German
(N=1612), Japanese (N=1292) twins and Italian (N=5657) families set heritability scores for
Aesthetic sensitivity as the highest of all subfactors, with 47 % of its variance explained by
genetic influences.
Interestingly, higher scores in both OE and Aesthetic Sensitivity is frequently
associated with a higher propensity of feeling chills (Colver & El-Alayli, 2016; Nusbaum &
Silvia, 2011; Silvia & Nusbaum, 2011). For example Silvia & Nusbaum (2011) and Nusbaum
& Silvia (2011) have both found in a sample of 196 individuals OE, measured respectively
by the Big Five Aspect Scale (DeYoung, Quilty, & Peterson, 2007) and by the NEO-FFI, as
the only significant predictor of the self-reported propensity of feeling aesthetic chills, as
measured by the chill factor score of the unusual AES . Findings that were later replicated by
Sachs et al. (2016) which found, using a sample of in 237 individuals, that OE measured by
the ten personality inventory (TIPI) was correlated with higher scores of the chill factor.
Further, Cukick & Bates (2014), using a sample of 952 hospitalized individuals, linked OE

PUTTING GENETICS IN AESTHETICS
15
with increased low-frequency heartbeat power, an indicator of increased sympathetic
autonomic activity, leading the authors to speculate on ANS function differences at resting
state that might influence the propensity of experiencing aesthetic chills. Finally, more direct
evidence comes from a study on 113 individuals by Colver et al. (2016), who found OE to be
the best overall predictor of aesthetic chills, measured both by self-report and physiological
correlates (GSR).
However, while different authors found OE as an important predictor of chills, some
inconsistencies are emerging from the literature. For example, Silvia et al. (2011) found
Aesthetic Sensitivity as the main predictor of aesthetic chills, while Colver et al.
(2016) found the Fantasy, Ideas and Values subfactors as the main predictor of it. Moreover,
Rickard (2004) found mean number of chills per minute positively correlated with
extraversion, and not Openness. Inconsistency that have been replicated in another study by
Sumpf (2015), who found a group of participants experiencing more chills, measured as a
moment of piloerection, to be higher in extraversion but not in OE
3
, contradicting both Silvia,
Colver and McCrae results.
It would be thus difficult to properly estimate the validity of such personality
measurement to capture the variation of aesthetic chills, and any inference based on the
genomic of personality would be mainly speculative. However, one of the items used for
measuring personality score, the item 188 of the NEO-PI-R, included in the shorter NEO-FFI
version as the number 43, ‘Sometimes when I am reading poetry or looking at a work of art, I
feel a chill or wave of excitement’(McCrae, 2007) directly refer to the experience of chills.
Such Item has been suggested to be a universal, yet diversified, marker of openness (McCrae,
2007). Indeed, cross-cultural studies found such item to be the first or the second-highest
loading item on OE on 31 out of 51 of the cultures examined, and to be the highest one the
combined sample, with a notable .55 overall median. Moreover, besides being indirectly
related through OE measured by the NEO-FFI to self-report (Nusbaum & Silvia, 2011) and
physiological measurement of chills experiences (Colver & El-Alayli, 2016), Item 43 has
been found to be a significant predictor of chills measured as GSR peak. Indeed, Colver et al.
(2016) found that the single item was apparently a better predictor for the number of chills
experiences during a musical piece than the whole subfactor Aesthetic Sensitivity and
3
However, the authors did find highest scores of openness for the groups who did experienced more aesthetic
chills when compared with a norming (standardized sample) sample (t(56) = 11.49, p <.001). Moreover,
extraversion resulted highest on the main self-selected group versus a group matched group. Since, no test was
conducted on the actual propensity of feeling aesthetic chills in the matched group the results of this author
should be considered with caution

PUTTING GENETICS IN AESTHETICS
16
Feelings. Finally, as stated above, people who score higher on the item have enhanced
resting-state brain functional connectivity between brain networks previously found to be
linked to structural individual differences underlying sensitivity to chills (Williams et al.,
2018). Beside the heightened connectivity between node of the salience network and the
ventral DMN, Williams et al. found higher scores on Item 43 to correlate with higher
connectivity of the latter with sensory networks, such as the superior temporal network and
the anterior visual network (all networks parcellation from (Thomas Yeo et al., 2011)).
That is, it seems that the Item 43 is measurement for responses largely shared, and yet
diversified, across cultures (McCrae, 2007), to be a valid measurement of the propensity of
experiencing aesthetic chills (Colver & El-Alayli, 2016), and to show its own pattern of
neurobiological differences (Williams et al., 2018).
1.5 Aim
Behavioural, physiological, neurobiological and personality factors have been
associated with aesthetic chills. Here we aim to explore if genetic effects can account for
individual differences in the propensity of feeling aesthetic chills. To partition such genetic
and environmental sources of variation, we conduct a biometric model fitting on twin data,
using Item 43 as an approximation (proxy) for the propensity of feeling chills. Item 43 was
chosen in favour to a broader personality measurement because the lack of reproducible
validity of the latter in capturing the variance of chills, and due to its success in explaining
behavioural and neurophysiological differences when compared with the boarder factors of
personality. Moreover, Since findings on both OE (Costa, Terracciano, & McCrae, 2001), the
Aesthetic Sensitivity sub factor (Costa et al., 2001; McCrae et al., 2005), and the reported
propensity of feeling chills (Panksepp, 1995) report females scores higher than males, we
hypothesized that scores on item 43 to differ between sexes. However, since no previous
work reported different etiological sources of variation per different sex on the propensity of
feeling aesthetic chills (i.e. different genetic or environmental factor to act for one sex but not
for the other), no hypothesis was formulated on the differences in the genetic and/or
environmental sources of variation.
Method
2.1 Participants

PUTTING GENETICS IN AESTHETICS
17
The data were obtained from the Netherlands Twin Register (NTR), a longitudinal
cohort established in 1987 by the department of biological psychology at the Vrije
Universiteit of Amsterdam. We analyzed data from three surveys, collected respectively in
2004 (survey 7) on 6760, 2009 (survey 8) on 10176 and 2013 (survey 10) on 9419 twin pairs
(Ligthart et al., 2019, in press). Table 1 shows the complete numbers of twins who completed
each survey. Zygosity in same-sex pairs was based on survey information on resemblance,
confusion by family members and friends and on DNA genotyping for part of the sample
(Ligthart et al., 2019, in press).
2.2 Materials
Data on the self-reported propensity of feeling aesthetic chills were collected by
mailed surveys (Ligthart et al., in press), during a period of 9 years. The study is a part of a
longitudinal inquire on the genetic and environmental architecture of etiological sources of
variation on a wide selection of traits (Ligthart et al., in press). Self-report of chills was
obtained from the short version of the NEO-personality Five Factor Inventory (NEO-FFI,
(Nusbaum & Silvia, 2011). The NEO-FFI consists of 60 items rated on a five-point scale (1–
5, totally disagree, disagree, neutral, agree and totally agree) and gives a score for the traits
Neuroticism, Agreeableness, Conscientiousness, Extraversion and Openness to Experience
(OE). The OE’s Aesthetic Sensitivity sub factor Item 43 ‘Sometimes when I am reading
poetry or looking at a work of art, I feel a chill or wave of excitement’ was selected as a
proxy for the propensity of experiencing aesthetic chills. As already stated above, we selected
this item because previous research suggested it to be a valid measurement of aesthetic chills
(Colver & El-Alayli, 2016; Silvia & Nusbaum, 2011), to be universally shared (McCrae,
2007), and yet diversified, with functional (Williams et al., 2018) and plausibly structural
putative associated differences (Sachs et al., 2016)
2.3 Procedures
To maximize the number of complete cases, survey 1, 2 and 3 were merged into one
combined datafile by randomized selection of twin pairs per survey. The randomized
selection of pairs followed the subsequent criteria: 1) we prioritized complete answers from
twin pairs for surveys at a specific time. For example, twin pairs were selected when both
twins reported scores for the Item 43 on one of the survey 1, 2 or 3. If both twins answered in
more than one survey (i.e. both for Survey 1, Survey 2), we randomly selected data for the
pair from one of the complete survey; 2) if one of the two twins response was missing for all

PUTTING GENETICS IN AESTHETICS
18
of the three surveys, we randomly selected a survey with the collected response for the other
twin. Finally, 26 cases were excluded due missing age. This resulted in the final sample N
(table 2) of age ranging from 15 to 97 years old with a mean of 30 (SD = 13). Of the reported
N, 5124 were complete twin pairs, 2786 MZ, 1305 same-sex DZ pairs, and 1033 opposite-sex
DZ were complete twin pairs
Table 2
Sample (N) of monozygotic (MZ) and Dizygotic (DZ) twin pairs per survey
Survey
N
pairs
MZ
male
MZ
female
DZ
male
DZ
female
DZ
opposite sex
Survey 1
6195
931
2463
478
1137
1186
Survey 2
9100
1239
3242
747
1684
2188
Survey 3
8302
1161
2935
663
1505
2038
Combined
8733
1231
2730
794
1592
2386
2.4 Genetic analysis
We used genetic biometric model fitting to estimate the relative proportion of
variance explained by genetic and environmental factors. Genetic modeling aims to partition
observed variance of a specific trait, namely phenotypic variance (P) to genetic and
environmental components (D. Boomsma, Busjahn, & Peltonen, 2002). On the one side,
genetic components are known to be either additive (A), i.e. the P explained by additive
effects of alleles at each genetic locus, or non-additive (D), that is the proportion of P that can
be accounted for by dominance (interactive) effects of alleles at contributing genetic locus.
On the other hand, environmental components are partitioned by either in shared (C), i.e. the
proportion of P that can be explained by shared environmental factors, or Unique (E), that is
the proportion of P that is explained by factors that are peculiar to the individual, and thus not
shared by each of the twins. Since E contains all the unexplained unsystematic variance, error
measurements are by definition captured by E.
This results in the final formulation of the phenotypic variance (see Knopik, Neiderhiser,
DeFries, & Plomin, 2016; Rijsdijk & Sham, 2002):
𝑉𝑃= 𝑉
𝐴+ 𝑉𝐷+ 𝑉
𝑐+ 𝑉𝐸

PUTTING GENETICS IN AESTHETICS
19
The twin design was used to estimate such genetic and environmental variance
components. The twin method is considered to be among one of the best natural experiment
that allows the partitioning of the observed phenotypic component by comparing
resemblances between MZ with DZ twin pairs raised together (D. I. Boomsma et al., 2006).
Since MZ twins derive form the same fertilize egg their genetic material is ~100% shared,
while only 50% on average of the genetic material inherited from their parents is shared
across DZ twins. Therefore, correlations between A within MZ twin pairs are always 1, while
within DZ twin pairs is always .5. For similar reasons, correlations between the D component
within MZ twin pairs will always be equal to 1, while for DZ pairs will be always .25 (See
Knopik, Neiderhiser, DeFries, & Plomin, 2016). Moreover, if twins are raised together, the
environment will be for both of the twins shared, thus the correlation between C within MZ
and DZ pairs will be 1 for both of the twin pairs. Finally, the correlation between E within
MZ and within DZ is always equal to 0, as a consequence of E being defined as the part of
the variance explained by factors that are unique to the individual, and thus not correlated.
This can be summarized by means of path diagrams, as shown in figure 2. That is, since the
only systematic etiological difference (under the assumptions of no gene-environment
correlation or interaction and no assortative mating (Rijsdijk & Sham, 2002)) between MZ
and DZ twin pairs is A, the higher the resemblance between MZ twins compared to DZ twins
the wider the proportion of P that can be accounted for by genetic components. However,
within the classic twin design (i.e., MZ and DZ twin pairs reared together) A, D, C, and E
cannot be simultaneously estimated (Knopik et al., 2016). We thus inspected the correlation
within twin pairs to predict which model was expected to estimate components estimated to
play a role in the propensity of reporting aesthetic chills (Knopik et al., 2016). Given that
variance and covariances of aesthetic chills were known, we thus tested which model fit the
data best by means of structural equation modeling.

PUTTING GENETICS IN AESTHETICS
20
Figure 2. Example of biometric univariate model path diagram. For simplicity D components, mean estimate
and covariates are not included. Squares represents the measured (known) phenotypic traits for both twin pairs.
Circles represent the genetic and environmental causal (latent) variance components. Straight arrows represent
the path coefficient. In this example path coefficients are fixed to 1 and the variance of the latent factors is
estimated. Curved lines between circles represent the hypothesized correlations within MZ or DZ twin pairs
between causal variance.
2.5 Biometric modelling
Biometric model fitting was carried out using the statistical package OpenMx (Boker
et al., 2019), in R-Studio version 1.2.1335. Item 43 mean, standard deviations, 95%
Confidence Intervals (CI) and within twin pair correlations were estimated within the
generated model (saturated model). The saturated model is the model who explain all the
observed variance (thus saturated) without trying to attempt to partition such variance to
genetic and environmental variance components. Given the significant effect of age on OE
scores (Costa & McCrae, 2006), age was used as a covariate. Significance of the covariate,
birth order effect and mean differences across zygosity were tested by a series of model
nested inside the saturated model. The goodness of fit of the model was evaluated both by
means of 1) the difference in minus twice the value of the log-likelihood (-2LL) between the
two distributions, which is used in log-likelihood ratio test (LRT) by keeping the model with
the p.value >.05 of the associated χ2 distribution. That is, the most parsimonious model that
was not significantly worse than the model with more parameters. 2) the Akaike’s
Information Criterion (AIC), by keeping the model with the lowest AIC as the best fitting
model.
Given that some authors found females to be more prone than male to experience
chills (Panksepp, 1995), that females scores higher on OE (Costa et al., 2001) and in the
Aesthetic Sensitivity sub factor (Costa et al., 2001; McCrae et al., 2005) a sex-limitation

PUTTING GENETICS IN AESTHETICS
21
model was carried out to examine whether mean or sources of etiological variation
statistically differed between the sexes. First, given the phenotypic correlation computed in
the saturated model, we fit the expected model with different mean and variance components
for male and female. Subsequently, we tested the model against the less parsimonious
saturated model following the procedure outlined in section 2.2. Finally, we tested for
goodness of the fit of the model by comparing it to the models with mean and variance
components equated to zero.
After sex effects were tested for significance, we compared the saturated model with
the full ACE model to partition the Item 43 P component into the expected genetic and
environmental variance components. Mean, standard deviations, 95% CI and within twin pair
correlations were also estimated in the biometric model. Subsequently, we test every other
nested model, which were obtained by constraining to zero one of the genetic or
environmental variance components. Heritability estimates were obtained by dividing A for
by the total observed variance P.
Results
3.1 Descriptive
Test-retest reliability were conducted on survey 1 and 2 (5 years apart), survey 2 and 3 (4
years apart) and survey 1 and 3 (9 years apart). Reliability estimates for item 43 range from
r(1842) = .57 (p < .001) between survey 1 and survey 2, r(2566) = .55 (p < .001) between
survey 2 and survey 3 and r(1354) = .53 (p < .001) between survey 1 and survey 3, all
considerably remarkable for a single item. All reliability measurement were calculated for
data from the firstborn twin alone. We excluded the second born from the analysis to avoid
biased correlation due family resemblance effects. Individuals score frequency on the 5
Likert-scale ranged from 21% (“strongly disagree”) to 4% (“strongly agree”), with the
majority of the individual (75%) distributed within the three central scores (see
supplementary material for the distribution of item 43 scores). The result that indicates 21%
of the firstborn twins to not report to feel aesthetic chills in the combined survey is in line
with previous random-population sampling studies on chills (Craig, 2005; Grewe et al.,
2009; Guhn et al., 2007; Silvia & Nusbaum, 2011; Wassiliwizky, Koelsch, et al., 2017),
which found one third or less of the tested sample to not report or not experience aesthetic
chills at all.

PUTTING GENETICS IN AESTHETICS
22
3.2 Biometric modelling
Phenotypic correlation extracted by the saturated model were r = .36 ([.33,.40] 95%
CI) within MZ and r = .15 ([.11,.19] 95% CI) within DZ twin pairs , suggesting genetic
sources of variation to play and shared environmental to not play a role in the variation of the
score on the item 43 ().
Table 3 shows the goodness of the fit comparison with the full saturated model. Removing
age as a covariate resulted in a deterioration of the model fit (-2LL = χ2(1) = 233.22.44, p <
.001). We thus confirmed the expected effect of age on item 43 mean scores. On the other
end, removing birth order and subsequently zygosity mean and variance differences did not
deteriorate the overall model fit (-2LL = χ2(8) = 2.95, p = .94; -2LL = χ2(16) = 8.43.44, p <
.94), indicating that mean and variance were not different across the first and the second-born
and across zygosity. As hypothesized, constraining mean scores to be equal across sexes
resulted in a deterioration of the fit (-2LL = χ2(17) = 106.17, p <.001), suggesting mean
scores between males and females to differ. Finally, constraining variance did not deteriorate
the overall fit of the model (-2LL = χ2(17) = 8.65, p = .95).
Table 3
Saturated model: Model-fitting results
Model
-2LL
df
χ2
Δdf
p
AIC
Saturated Model
40164.73
13144
NA
_
_
13876.73
No covariate
40397.95
13145
233.22
1
<.001
14107.95
Birth order: mean
40165.91
13148
1.17
4
.88
13869.91
Birth order: mean variance
40167.69
13152
2.96
8
0.94
13863.69
Zygosity: mean
40172.45
13156
7.72
12
0.81
13860.45
Zygosity: mean variance
40173.16
13160
8.43
16
0.93
13853.16
Sex: mean
40270.90
13161
106.17
17
<.001
13948.90
Sex: variance
40173.38
13161
8.65
17
0.95
13851.38
Note. In bold best-fitting model. Models are reclusively nested starting from the most parsimonious model. For
example, the ‘birth order: mean & variance model’ is nested from the most parsimonious ‘birth order: mean’,
while ‘birth order: mean’ is not nested in the “No covariate” model, since removing the covariate results in a
deterioration of the overall fit.
Phenotypic correlations, extracted by the saturated model, was r = .39 ([.34,.44] 95% CI) and
r = .35 ([.31,.38] 95% CI) for MZ male and MZ female respectively, r = .07 ([.00,.16] 95%

PUTTING GENETICS IN AESTHETICS
23
CI) and r = .20 ([.14,. 25] 95% CI) for DZ male and DZ female respectively, and r = .13
([.08,.19] 95% CI) for opposite sex DZ. Correlation and CI suggested either ADE (rMZ male >
½ rDZ male) or AE models (considering the 95 %CI) for male, and an ACE (rMZ female < ½ rDZ
female) or AE models (considering 95% CI) for female. Taking into account the overall
phenotypic correlation (r = .36 ([.33,.40] 95% CI) within MZ and r = .15 ([.11,.19] 95% CI) )
we opted to fit an AE model for both male and female. Table 4 shows the results for the sex
limitation models. As hypothesized, mean was found to differ across sexes (-2LL = χ2(1) =
96.70, p = <.001). However, constraining variance components (A and E) to be equal did not
deteriorate the model fit (-2LL = χ2(2) = 0.77, p = .68). Thus, we did not find etiological
sources of variation to differ between male and female.
Table 4
Biometric model: model-fitting results for the sex limitation and the univariate models
Model
minus2LL
-2LL
χ2
Δdf
p
AIC
h2
c2
e2
Sex limitation model
Saturated
Model
40181.85
13164
-
-
-
13876.30
-
-
-
AE
40181.08
13163
_
_
_
13855.08
-
-
-
AE: mean
40277.78
13164
96.70
1
<.001
13949.78
-
-
-
AE: variance
40181.85
13165
0.78
2
.68
13851.85
-
-
-
Univariate biometric model fitting
Saturated
Model
40181.85
13164
-
-
-
13876.73
-
-
-
ACE
40181.85
13164
17.12
20
.64
13853.85
.35
.00
.65
AE
40181.85
13165
1.60
1
>.999
13851.85
.35
-
.65
CE
40248.29
13165
66.44
1
<.001
13918.29
-
.26
.73
E
40606.77
13166
424.92
2
<.001
14274.76
-
-
1
Note. In bold best-fitting models. h2 heritability estimate; c2 shared systematic environment estimate, e2 unique
unsystematic environmental estimate. The expected AE sex limitation model and the full univariate ACE model
are tested against the full saturated model. Nested sex limitation and univariate models are tested against the
expected AE and the full ACE respectively
The final genetic univariate model fitting results and comparison are presented in table 3.
Constraining variance components A, and A and C to zero respectively deteriorated the
model fit (-2LL = χ2(1) = 66.43, p = <.001; -2LL = χ2(2) = 424.92, p = <.001). Thus, as

PUTTING GENETICS IN AESTHETICS
24
expected the final model AE (figure 3), with mean estimates adjusted for age (b=1.25) equal
to 2.04 (SD = 1.13) for male and 2.26 (SD = 1.13) female, was the most parsimonious one.
As shown in table 4, heritability estimate for the propensity of feeling chills is 35 % (A = .35
[.34, .36] 95% CI), while the remaining 65% of the phenotypic variance ( E = .65 [.64, .65]
95% CI) can be accounted for by unsystematic effects, such as environmental experience
unique to the individual or measurement errors.
Figure 3 final AE biometric model. Twin (T) 1 and 2 phenotypic variance is represented by squares. Triangles
represent mean estimates for male (mm) and female (mf). Small circle represents the covariate (cov) age,
straight arrows represent path coefficients. Circles represent additive genetic (A) and unsystematic (E) variance
components. Percentages of partitioned variance is reported inside the circles. Variance component values are
represented by the curved arrows wrapping the circles. Curved arrows between circles represents correlations
between genetic and environmental components of both MZ and DZ twins.
Discussion
4.1 Genetic sources of variation on aesthetic chills
This research is the first study who investigated and reported genetic and environmental
sources of variation in the propensity of feeling aesthetic chills. We found 35% of the
variance of the NEO-FFI Item 43 ‘Sometimes when I am reading poetry or looking at a work
of art, I feel a chill or wave of excitement’, which we used as a proxy measurement for the
propensity of feeling chills from aesthetic phenomena, to be accountable for by additive

PUTTING GENETICS IN AESTHETICS
25
genetic factors. One the one side, this result is one of the first evidence ever reported in the
literature on genetic sources of variation in individual differences of aesthetic experiences.
On the other end, this study is in line with previous personality studies, in which the
personality factor OE and the subfactor Aesthetic Sensitivity, both predictor of aesthetic
related trait variation (Chamorro-Premuzic et al., 2009; Fayn et al., 2015; Silvia & Nusbaum,
2011), have been consistently found to be relatively highly hereditable (McCrae et al., 2011).
Here we argue that, as has been argued for personality trait (Fayn et al., 2015) the genetic
factors found as in this study might exert their influence on the propensity of feeling chills by
lowering a certain threshold (‘chill threshold’) that would make such experiences more likely
to happen. By some regards, we apply the hypothesis of Feist (Feist, 2006:112), who thought
that personality traits worked as threshold traits, that is likelihood traits that make
behavioural display more probable. In our specific case, since aesthetic chills have been
described as peak phenomena, it is possible to argue that such genetic factors directly or
indirectly influence the threshold of such peaks. That is, genetic influences will not be the
cause of aesthetic chills, but rather a predisposition for a higher likelihood.
However, no conclusions regarding putative mechanisms underlying the genetic factors
influencing aesthetic chills as found in this study can be drawn. While physiological (Čukić
& Bates, 2014) and neurobiological (Sachs et al., 2016; Williams et al., 2018) individual
differences have been reported in the literature, it is too early to suggest which plausible
candidate is unique to chills and not shared among more general traits, (i.e. reward
sensitivity). For example, while intervention on mu-opioid receptors might interfere with the
probability of feeling aesthetic chills (Goldstein, 1980; Mallik, Chanda, & Levitin, 2017), it
might also decrease the probability of experiencing general pleasure (Chelnokova et al.,
2014). It would be thus difficult to make claims on the specificity of such biological
mechanism on chills, without falling in simplistic conclusions. Moreover, considering the
fact that the item investigated as in this study does not load on Extraversion (McCrae, 2007)
and that genetic sources of variation accounting for by such personality trait mediate
hedonistic interests, such as getting pleasure from watching television, but not artistic-
creative one, such as be more prone to go to museum and exhibition (Kandler, Bleidorn,
Riemann, Angleitner, & Spinath, 2011), conclusions on biological structures underpinning
pleasurable experiences alone might be hazardous. A cautionary approach should be applied
in suggesting genetic influences to affect dopaminergic activity too. For example, while
several studies posit variation in dopamine related structures as genotypic causative elements
influencing a variety of traits such as creativity and openness (Spee et al., 2018; Vartanian,

PUTTING GENETICS IN AESTHETICS
26
2018), no large study to date has been able to consistently replicate or show significant effect
of genetic variation on such factors (Lo et al., 2017). Such inconsistency deriving from
candidate genetic approaches or speculative reasoning might be explained by the highly
polygenetic nature of genetic influences (Visscher et al., 2017), that is by the recent findings
which indicate effects of rare variants, genetic variation feebly shared among the population,
to account for most of the variance explained by genetic factors. This should therefore serve
as a cautionary tale for making claims on putative mechanisms on traits such as the one
investigated. Nevertheless, the findings of Williams (2018) on the heightened connectivity, in
individual who scores higher on the Item 43, and the ventral side of the DMN and the
Salience, Superior Temporal and Anterior Visual networks
4
might suggest a stimulating area
for further research. Indeed, while the author concluded that such enhanced connectivity
might reflect a ‘better integration of environmental perception with internal emotional
experience’ (Williams et al., 2018:162), whether such better integration is the effect of
genetic influence is still not known. Thus, by comparing the results of such analysis between
MZ and DZ, which were present in the dataset used by Williams (Williams et al., 2018:159),
further research might tighten the link between putative mechanism and heritability estimates
on chills, thus allowing more accurate prediction on the underlying genetic sources of
variation to be drawn.
4.2 Environmental source of variation
While more than one third of the variance has been accounted for by genetic effects,
the remaining 65% of the variance in aesthetic chills can be accounted for by unsystematic
environmental sources of variation. However, we found no shared systematic environmental
effects to play a role in individual differences on aesthetic chills. While the finding that
familiar influences do not play a role in the propensity of feeling chills seems to be
counterintuitive, several explanations can be put forward. Firstly, this is quite consistent with
most of the investigated trait, which reports moderately small to absent effects of the shared
environment on trait variation (Polderman et al., 2015). Moreover, we know that whether the
trait of feeling chills seems to be a consistent yet diversified item (McCrae, Nusbaum), what
triggers such experiences is peculiar to the individual (Bannister, 2018). This is exemplified
4
Note that the authors report significant partial correlation, controlling for the effects of age, gender, head
motion and, more importantly personality factors, between the ventral DMN and the Anterior
Visual(ρ=0.10**), ventral DMN and the Superior Temporal(ρ=0.09**), and DMN and the Salience
network(anterior cingulo-opercular, ρ=0.09**; posterior cingulo-opercular, ρ=0.10**). **p < .01

PUTTING GENETICS IN AESTHETICS
27
by choices taken by most of the researcher in the field of chills (Blood & Zatorre, 2001;
Grewe et al., 2009; Rickard, 2004; Salimpoor et al., 2011; Sumpf et al., 2015), which
preferred self-selected stimuli over experiment-selected stimuli as a condition to elicit chills.
That is, in several experiments, researchers asked the participants to bring music, poetry, and
film clips that were able to elicit chills. Such self-selected triggers were then used as a control
condition for other individuals, since the ones which worked for some individual, did not
worked on average for any of the other ones. This highly idiosyncratic component of what
might elicit chills is in line with the findings that unique experiences (E) are the main source
of influences and can be put forward as an explanation for why shared environmental effects
do not play a role. However, whether we were able to quantify the amount of unsystematic
effects on the propensity of feeling aesthetic chills, and to explain the relevance of unique
exposure on what can elicit chills, part of the role of such effects is yet elusive. Moreover,
due to the design of the study, and given the reliability estimates of the Item 43, we are not
able to infer how much of the unsystematic component is explained by environmental
influences or is just a consequence of measurement error.
4.3 Sex differences
Further, we found female more prone than male to experience aesthetic chills. This is
in line with some of the results previously reported in the literature. Panksepp (1995), for
example suggested such differences to be accounted for by what he called the ‘separation
call’ phenomena. He argued that chills evolved over time from a need of physical closeness,
and that the mother-infant relationship should have influenced such propensity till the point
of an enhanced selected propensity of experiencing chills in female. While the consensus on
this hypothesis is far from be ubiquitous (see Nusbaum & Silvia, 2013), our results partially
support this hypothesis. However, two considerations are worthy of attention when
interpreting such results. First, the association made by Panksepp between physical
closeness-distancing (which is associated respectively with rise and drop in temperature) and
chills was based on the assumption, probably based on the terminology used to describe such
phenomenon, that moment of aesthetic chills are reflected by a physiological drop in
temperature. Though, as reflected in table 1, skin temperature differences between chill and
no chill moments rarely reach significance. Finally, it is worth noting that while female score
higher than male in the propensity of feeling chills, the genetic and environmental sources of
variation did not differ across the two sex. That is, the same amount of genetic and

PUTTING GENETICS IN AESTHETICS
28
environmental variance can account for the same amount of phenotypic variance in male and
in female.
4.4 Limitation & further research
First, several limitations were implied as a consequence of some of the assumptions of the
twin design (see Rijsdijk & Sham, 2002). The univariate twin model assumes the
environment to be equally shared between the twins and genetic sources of variation to not
interact with environmental ones. However, gene environment correlations, i.e. when a
certain genotype indirectly influences the likelihood of self-selecting, or of being in, a certain
environment, might play a role in differentiating the propensity of getting aesthetic chills. For
example, since aesthetic chills are highly pleasurable experiences, individuals that are more
likely to experience chills will surround themselves with an environment that increase the
chance of repeating such experiences. Thus, the offspring of such individuals will inherit not
only predispositions towards the likelihood of experiencing more chills, but also an
environment in which such experiences should be more likely to happen. For similar reason
non-random mating, i.e. mating between individuals that genotypically resemble themselves,
might bias etiological estimates, since DZ twins would share more than 50% on average of
genetic material. However, the consequence of violation of such assumptions (gene x
environment correlation and non-random mating) results in overestimation of both MZ and
DZ correlations which should end into higher C estimates (Rijsdijk & Sham, 2002).
Therefore, since we did not find C to account for any of the phenotypic variance, it is likely
that such assumptions have not been violated. Finally, the twin model assumes the interaction
between environment and genetic material (GxE) to be trivial. That is, no GxE assume
genetic variance to be independent of any environmental moderator. However, unsystematic
exposure to aesthetic phenomena at a certain developmental stage might influence the effect
of genetic materials on the propensity of getting chills. Since violation of such assumptions
would be estimates in E (Rijsdijk & Sham, 2002), and E has been found to account for more
than half of the variance in aesthetic chills, no conclusion on the validity of such assumption
can be drawn in this study. It is worth mentioning, that for the same reason, violation of such
assumption would not decrease our estimate of A. That is, while the estimate of E as reported
in this study might be overestimated, the estimate for heritability would not decrease, but
only increase.
Secondly, it is not clear weather test-retest reliability is a good reflection of the
reliability of the measurement per-se, or if the propensity of feeling aesthetic chills is subject

PUTTING GENETICS IN AESTHETICS
29
to change over time. In case of a longitudinal effects on such propensity then E as estimated
in this study would be confounded with development sources of variation, and thus
overestimated. Thus, developmental studies are needed to test such hypothetical changes over
time. Beside developmental studies on the tendency of the propensity of feeling aesthetic
chills to change over time, further hypothesis-free studies, such genomic association studies
(GWAS) should try to explore the whole genome to inspect which, likely polygenic, variation
can be associated to variation in such phenomenon.
4.5 Conclusion
Chills have been used as successful marker in the field of aesthetic science to explore
physiologically (Goldstein, 1980; Grewe et al., 2009) and neurobiological (Blood & Zatorre,
2001; Salimpoor et al., 2011) correlates of intense aesthetic experiences, as well as
neurophysiological (Sachs et al., 2016; Williams et al., 2018) and personality (Colver & El-
Alayli, 2016; Silvia & Nusbaum, 2011) differences that influence the degree to which such
intensity is experienced. Here we showed the time has come for the field of aesthetic science
to accept that genetics play a role in the individual differences in the propensity of feeling
aesthetic experiences. The propensity of feeling aesthetic chills is not the result of
deterministic/innate genetic effects, nor the effect of purely environmental exposure or the
consequences of unique experiences. It is a result of genetic predisposition and unsystematic
experiences. To neglect the influence of such genetic effects is to neglect an aspect that
defines our biological self.

PUTTING GENETICS IN AESTHETICS
30
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