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ABSTRACT We significantly enriched ChillsDB, a dataset of audiovisual stimuli validated to elicit aesthetic chills. A total of 2,937 participants from Southern California were exposed to 40

stimuli, consisting of 20 stimuli (10 from ChillsDB and 10 new) presented either in audiovisual or audio-only formats. Questionnaires were administered assessing demographics, personality

traits, state affect, and political orientation. Detailed data on chills responses is captured alongside participants’ ratings of the stimuli. The dataset combines controlled elicitation of

chills using previously validated materials with individual difference measures to enable investigation of predictors and correlates of aesthetic chills phenomena. It aims to support

continued research on the mechanisms and therapeutic potential of aesthetic chills responses. SIMILAR CONTENT BEING VIEWED BY OTHERS CHILLSDB: A GOLD STANDARD FOR AESTHETIC CHILLS STIMULI

Article Open access 20 May 2023 ON THE UNIVERSALITY OF AESTHETIC PREFERENCE AND INFERENCE: A CROSS-CULTURAL (CHINESE–GERMAN) STUDY Article Open access 17 May 2025 COMPARATIVE ANALYSIS OF

AESTHETIC PREFERENCES IN CHINESE FREEHAND PAINTINGS AMONG CHINESE RETIRED ELDERS BEYOND GENDER Article Open access 04 April 2025 BACKGROUND & SUMMARY Aesthetic chills are a universal

marker of human peak experiences across domains and cultures1,2,3. Characterized by goosebumps and cold shivers down the spine, chills are psychogenic bodily reactions triggered by engaging

with evocative stimuli like music or stories2,4,5. As a conscious, measurable emotion with neural and behavioral correlates, chills show promise for elucidating the relationship between

physiology and affect1,6,7, and for the enhancing positive affect in clinically relevant populations8,9. Studies indicate chills increase altruism, pleasure, attention and memory10,11,

modulate heart rate, pupils, skin conductance and muscle contractions12,13,14, and can generate positive shift in mood and emotion in depression8. Despite the richness of the phenomenon,

most research has been limited to music as the primary stimulus6,7,10. Several databases of stimuli exist to elicit emotions in the laboratory (see review in Table 1). However, none of these

databases is focused exclusively on chills making it difficult for researchers interested in this phenomenon to reliably induce the emotion in populations of interest. To fill this gap,

ChillsDB15 was designed to offer a validated database of audiovisual aesthetic chills stimuli using a novel approach of mining social media content. The original ChillsDB tested 204

potential stimuli across 600 participants. In the current iteration, data was collected from a significantly larger sample of 2,937 participants, all based in Southern California, and

balanced for an even representation of political orientation, sex, age and education level. Participants were presented with 20 stimuli, consisting of a subset of 10 stimuli drawn from the

previously validated set as well as 10 new stimuli. A total of 20 stimuli were presented to participants in two formats-audio only and audiovisual-resulting in 40 total stimulus

presentations with each format represented equally. Additional participant data was collected concerning demographics, personality trait dispositions, and political orientation.

Stimulus-related data included pre- and post-stimulus state affect, valence, and mood ratings as well as questionnaires aimed at characterizing post-stimulus state phenomenology. This

expansion of the dataset allows for a more in-depth exploration of trait, state and demographic factors affecting aesthetic chills, as well as associated phenomenology Fig. 2. METHODS

DATABASE DESIGN We selected 40 stimuli (20 audio and 20 audiovisual) combining a subset from the original Chills DB (N = 10) and a novel subset obtained from additional parsing using the

ChillsDB method and internal polling (N = 10). Each of the 20 stimuli was presented to participants in either two formats - audiovisual or audio-only - in order to compare and test for

differences between the two presentation modalities. ChillsDB stimuli were harvested from online social media platforms, YouTube and Reddit, using a Python-based tool to find stimuli

distributed across social media platforms using breadth-first search algorithm16 (textcolorredNote that all the stimuli used in this experiment were extracted from YouTube). To validate the

stimulus set, we used the Qualtrics online platform to recruit participants. For more details on harvesting, refer to the original ChillsDB article15. PARTICIPANTS A total of 3,259

participants initially took part in the experiment. Participants were recruited and compensated through Qualtrics. Recruitment was performed according to the following quotas: southern

california residents only, approximately equal gender distribution (though gender fluid, nonbinary, and non reporting respondents were also included), and racial/ethnic distributions

approximately conforming to southern california census data. Participants in this study were compensated the equivalent of $12 per hour for their participation in the study. Given initial

piloting suggesting an average of 20–30 minutes for completion, all participants were compensated $8 for their responses to ensure sufficient, consistent compensation. Payment was made via

the Qualtrics platform as follows: Panelists join from a variety of sources. They may be airline customers who chose to join in reward for SkyMiles, retail customers who opted in to get

points at their favorite retail outlet, or general consumers who participate for cash or gift cards, etc. When participants are invited to take a survey, they are informed what they will be

compensated before they enter the survey. We assessed the data validity by adding qualitative questions (“If you experienced chills, please explain what in the video gave you chills and why

you think that is”). These questions were carefully reviewed by two experts and subjects whose response were incoherent with their report were deleted from the dataset. Following this

initial check, we identified 932 participants who reported a non-zero Chills Intensity (Mean I = 19.6; SD = 25) despite reporting that they did not experience chills. This was further

corroborated in their qualitative descriptions, where the majority stated explicitly they did not experience chills. We eliminated all instances (N = 219) where Chills Intensity exceeded 1

standard deviation from the mean (Mean I = 10.3, SD = 20.7). We retained only those participants (N = 656) whose qualitative responses unambiguously confirmed the absence of chills. These

participants were placed in the ‘No Chills’ category and were subsequently omitted from the Chills Intensity analysis. A small number (N) of subjects were eliminated who reported 0 chills

intensity despite reporting chills as this indicated an unreliable responder. Following these data cleaning procedures, the experiment involved a diverse group of 2,937 participants, all of

whom hailed from Southern California (see Tables 2, 3 for the age and education distributions). The gender distribution was fairly balanced, with 54.24% identifying as female and 41.44% as

male. In terms of political affiliation, the largest group identified as Democrats (50.66%), followed by Republicans (21.59%), and Independents (14.81%). Notably, 11.64% of participants did

not specify a political affiliation, while a small proportion (1.19%) identified with other political affiliations. Political Orientation was probed as well and is reported in the database.

In regards to racial identity, the majority of participants (68.44%) identified as White or Caucasian. The second largest racial group was those identifying as Other (11.37%), followed by

American Indian/Native American or Alaska Native (4.97%), and Black or African American (1.46%). A small percentage of participants (0.31%) identified as Asian. Given this broad demographic

range, the present dataset provides a rich, representative sample for examining the phenomena under investigation in the context of Southern California. ETHICS The experiment was conducted

in compliance with the Helsinki Declaration. The protocol was granted an exemption status (Advarra IRB Exemption Pro00068209). All participants gave their voluntary informed consent and

procedures followed the Ethics Code of the American Psychological Association. All participants were informed about the purpose of the research, their right to decline to participate and to

withdraw from the experiment, and the limits of confidentiality. We also provided them with a contact for any questions concerning the research and with the opportunity to ask any questions

regarding the phenomenon under study (aesthetic chills) and receive appropriate answers. DATA RECORDS DATASET STRUCTURE The ChillsDB 2.0. dataset is released under a Creative Commons

Attribution 4.0 International (CC BY 4.0) license on FigShare17. This allows others to freely share and adapt the dataset as long as appropriate credit is given to the original creators by

citing the published paper (Schoeller _et al_., 2023). The dataset is divided into two .csv files available under a CC BY 4.0 license on the associated FigShare17. For a comprehensive

understanding of each column, researchers are advised to refer to the HEADER EXPLANATION FILE 5 (see Table 5). * 1. DATA FILE: This file contains the primary data collected from

participants. * 2. TRAITS QUESTIONNAIRES FILE: This file contains the full questionnaires assessing personality traits including DPES, MODTAS, KAMF, and NEO-FFI that were completed by

participants. * 3. HEADER EXPLANATION FILE: This file provides explanations and more detailed descriptions for each of the columns in the data file. * 4. STIMULI FILE: This file provides

explanations and URL to the stimuli on a private YouTube channel (contact the authors for the.mp4). STIMULI The stimuli are listed in the Stimuli.csv file in the dataset (see also Table 4

and Fig. 1). Due to copyright reasons, all stimuli were stored on a private server. Please contact the authors to be granted access to the.mp4 of the stimuli. We curated 40 stimuli (20 audio

and 20 audiovisual), of which 10 each were directly sourced from the original Chills DB. To compare the efficacy of individual expert recommendations against crowd-sourced ones, we

solicited individual suggestions from the study authors and their extended social networks regarding potential stimuli eliciting aesthetic chills. We then identified overlaps in these

recommendations, which were selected to complete the remaining 10 (N = 10) stimuli for our study. TECHNICAL VALIDATION Participants were recruited for this study through an online platform

(Qualtrics.com) with a focus on individuals residing in Southern California (see Participants section). Before proceeding with the study, participants underwent an initial screening to

confirm their geographical location and provided their informed consent. Participants were then asked to provide basic demographic information including gender, education level, and age.

Additionally, participants were queried about their political orientation and whether they were affiliated with any political party. In order to assess participants’ affective state, they

were prompted to indicate their levels of valence and arousal. Subsequently, participants completed trait questionnaires, including the _Disposition Positive Affect (DEPS)_18, _NEO

Five-Factor Inventory (NEOFFI)_19, _Modified Tellegen Absorption Scale (MODTAS)_20, and _Kama Muta Questionnaire (KAMF)_21. Participants were then randomly assigned to one of 40 stimulus

conditions. After exposure to the assigned stimulus, participants were asked to report their emotional state in terms of valence and arousal once again. They were also asked to indicate how

much they liked the video, whether they had seen the video previously, whether they experienced chills while watching the video, and if so, to rate the frequency and intensity of their

chills. Participants were also asked whether the video reminded them of a personal experience, and if they experienced goosebumps or tears, they were asked to indicate what elicited those

responses. Following the assessment of participants’ immediate responses to the stimulus, they were directed to complete a set of state questionnaires including the _Watts Connectedness

Scale_22, _Ego Dissolution Scale_23, and _Kama Muta Scale_24. Upon completion of the study, participants were thanked for their participation and provided with appropriate remuneration for

their time and effort. The average duration of each experiment was approximately 40 minutes. USAGE NOTES The ChillsDB 2.0 database builds upon the foundation established by the original

ChillsDB, offering an extended scope for aesthetic chills research. Identifying and validating stimuli that can robustly elicit positive affective states as well as phenomenological states

such as ego-dissolution, connectedness and moral elevation, is of value to cognitive and affective neuroscience. Being able to control and manipulate these stimuli in a laboratory setting

provides researchers the experimental control needed to map precise relationships between neural activity and phenomenology. Without standardized, validated stimuli capable of provoking

robust and measurable affective reactions under controlled conditions, researchers lack a solid basis for elucidating the complex neurobiology underlying human emotion and peak experiences.

Indeed, future research should further validate these stimuli by including objective physiological measures in addition to the subjective report that formed the basis of our analysis Table 

5. While the initial ChillsDB was centered on identifying and validating chills-eliciting content, ChillsDB 2.0 provides a more comprehensive perspective via a rich set of trait predictors

and state correlates 2. Notably, a leading stimulus in this updated database demonstrates a 0.75 probability of inducing chills across over 70 participants. This database may also have

clinical relevance where this expanded database can contribute to research in areas like depression, where aesthetic chills have been shown to mitigate maladaptive cognition25, improve

hedonic tone26, and aberrant chills response may be a physiological signature of anhedonia8. Chills are also a key target for the development of body-based, interoceptive technologies

enhancing the distinct somatic markers of the emotion9,27. At the social scale, deep analysis of chills-eliciting materials could elucidate themes and narratives centrally important to human

meaning-making3,28. The stimuli that reliably elicit aesthetic chills, as catalogued here, may tap into content that resonates deeply for individuals and cultures?. With a larger

participant sample and detailed individual difference measures, ChillsDB 2.0 may facilitate a deeper understanding of the factors influencing chills experiences, potentially bridging

neuroaesthetics and broader psychological research29. CODE AVAILABILITY The code for parsing YouTube and Reddit networks is available under an MIT license at

https://github.com/ChillsTV/AffectiveStimuliScraper. REFERENCES * Schoeller, F. Knowledge, curiosity, and aesthetic chills. _Frontiers in psychology_ 6, 1546 (2015). Article  PubMed  PubMed

Central  Google Scholar  * McCrae, R. R. Aesthetic chills as a universal marker of openness to experience. _Motivation and Emotion_ 31, 5–11 (2007). Article  Google Scholar  * Schoeller, F.

The shivers of knowledge. _Human and Social Sciences_ https://doi.org/10.1515/hssr-2015-0022 (2015). * McPhetres, J. & Zickfeld, J. H. The physiological study of emotional piloerection:

A systematic review and guide for future research. _International Journal of Psychophysiology_ 179, 6–20, https://doi.org/10.1016/j.ijpsycho.2022.06.010 (2022). Article  PubMed  Google

Scholar  * Schoeller, F., Haar, A. J. H., Jain, A. & Maes, P. Enhancing human emotions with interoceptive technologies. _Phys. Life Rev._ 31, 310–319 (2019). Article  ADS  CAS  PubMed 

Google Scholar  * Blood, A. J. & Zatorre, R. J. Intensely pleasurable responses to music correlate with activity in brain regions implicated in reward and emotion. _Proceedings of the

national academy of sciences_ 98, 11818–11823 (2001). Article  ADS  CAS  Google Scholar  * Salimpoor, V. N., Benovoy, M., Longo, G., Cooperstock, J. R. & Zatorre, R. J. The rewarding

aspects of music listening are related to degree of emotional arousal. _PloS one_ 4, e7487 (2009). Article  ADS  PubMed  PubMed Central  Google Scholar  * Schoeller, F., Jain, A., Maes, P.

& Reggente, N. Exploring aesthetic chills as a biomarker in depression. _PsyArXiv_ https://doi.org/10.31234/osf.io/x26df (2023). * Jain, A., Schoeller, F., Zhang, E. & Maes, P.

Frisson: Leveraging metasomatic interactions for generating aesthetic chills. In _Proceedings of the 2022 International Conference on Multimodal Interaction_, 148–158,

https://doi.org/10.1145/3536221.3556626 (2022). * Fukui, H. & Toyoshima, K. Chill-inducing music enhances altruism in humans. _Frontiers in psychology_ 5, 1215 (2014). Article  PubMed 

PubMed Central  Google Scholar  * Sarasso, P. _et al_. Beauty in mind: Aesthetic appreciation correlates with perceptual facilitation and attentional amplification. _Neuropsychologia_ 136,

107282 (2020). Article  CAS  PubMed  Google Scholar  * Sumpf, M., Jentschke, S. & Koelsch, S. Effects of aesthetic chills on a cardiac signature of emotionality. _PLoS One_ 10, e0130117

(2015). Article  PubMed  PubMed Central  Google Scholar  * Benedek, M. & Kaernbach, C. Physiological correlates and emotional specificity of human piloerection. _Biological psychology_

86, 320–329 (2011). Article  PubMed  PubMed Central  Google Scholar  * Zickfeld, J. H., Arriaga, P., Santos, S. V., Schubert, T. W. & Seibt, B. Tears of joy, aesthetic chills and

heartwarming feelings: Physiological correlates of kama muta. _Psychophysiology_ 57, https://doi.org/10.1111/psyp.13662 (2020). * Schoeller, F. _et al_. Chillsdb: A gold standard for

aesthetic chills stimuli. _Sci Data_ 10, 307 (2023). Article  PubMed  PubMed Central  Google Scholar  * Cormen, T. H. 22.2 breadth-first search. (2009). * Schoeller, F., Christov-Moore, L.,

Lynch, C. & Reggente, N. ChillsDB 2.0: Individual Differences in Aesthetic Chills Among 2,900+ Participants in Southern California, _Figshare_,

https://doi.org/10.6084/m9.figshare.23935611.v1 (2023). * Dixson, D. D., Anderson, C. L. & Keltner, D. Measuring positive emotions: An examination of the reliability and structural

validity of scores on the seven dispositional positive emotions scales. _Journal of Well-Being Assessment_ 2, 115–133 (2018). Article  Google Scholar  * Murray, G., Rawlings, D., Allen, N.

B. & Trinder, J. Neo five-factor inventory scores: Psychometric properties in a community sample. _Measurement and evaluation in counseling and development_ 36, 140–149 (2003). Article 

Google Scholar  * Jamieson, G. A. The modified tellegen absorption scale: A clearer window on the structure and meaning of absorption. _Australian Journal of Clinical and Experimental

Hypnosis_ 33, 119 (2005). Google Scholar  * Zickfeld, J. H. _et al_. Kama muta: Conceptualizing and measuring the experience often labelled being moved across 19 nations and 15 languages.

_Emotion_ 19, 402 (2019). Article  PubMed  Google Scholar  * Watts, R. _et al_. The watts connectedness scale: a new scale for measuring a sense of connectedness to self, others, and world.

_Psychopharmacology_ 239, 3461–3483 (2022). Article  CAS  PubMed  PubMed Central  Google Scholar  * Nour, M. M., Evans, L., Nutt, D. & Carhart-Harris, R. L. Ego-dissolution and

psychedelics: validation of the ego-dissolution inventory (edi). _Frontiers in human neuroscience_ 10, 269 (2016). Article  PubMed  PubMed Central  Google Scholar  * Zickfeld, J. H.,

Schubert, T. W., Seibt, B. & Fiske, A. P. Kammus: A multiplex measure of kama muta. _Emotion_ (2019). * Schoeller, F., Jain, A., Adrien, V. & Maes, P. Aesthetic chills foster

self-acceptance and emotional breakthrough in depression. _PsyArXiv_ https://doi.org/10.31234/osf.io/rhftq (2022). * Jain, A. _et al_. Aesthetic chills cause an emotional drift in valence

and arousal. _Front. Neurosci_. 16, https://doi.org/10.3389/fnins.2022.1013117 (2023). * Schoeller, F. _et al_. Interoceptive technologies for clinical neuroscience. _PsyArXiv_

https://doi.org/10.31234/osf.io/sqr6z (2022). * Schoeller, F. The satiation of natural curiosity. _International Journal of Signs and Semiotic Systems_ 5, 27–34,

https://doi.org/10.4018/ijsss.2016070102 (2016). Article  Google Scholar  * Sarasso, P., Francesetti, G. & Schoeller, F. Editorial: Possible applications of neuroaesthetics to normal and

pathological behaviour. _Front. Neurosci._ 17, 1225308, https://doi.org/10.3389/fnins.2023.1225308 (2023). Article  PubMed  PubMed Central  Google Scholar  * Koelstra, S. _et al_. Deap: A

database for emotion analysis;using physiological signals. _IEEE Transactions on Affective Computing_ 3, 18–31, https://doi.org/10.1109/T-AFFC.2011.15 (2012). Article  Google Scholar  *

McCurrie, C., Crone, D., Bigelow, F. & Laham, S. Moral and affective film set (maafs): A normed moral video database. _PLoS One_ 13, e0206604,

https://doi.org/10.1371/journal.pone.0206604 (2018). Article  CAS  PubMed  PubMed Central  Google Scholar  * Chen, H., Chin, K. L. & Tan, C. B. Selection and validation of emotional

videos: Dataset of professional and amateur videos that elicit basic emotions. _Data in Brief_ 34, 106662, https://doi.org/10.1016/j.dib.2020.106662 (2021). Article  CAS  PubMed  Google

Scholar  * Negrão, J. G. _et al_. The child emotion facial expression set: A database for emotion recognition in children. _Frontiers in Psychology_ 12, 666245,

https://doi.org/10.3389/fpsyg.2021.666245 (2021). Article  PubMed  PubMed Central  Google Scholar  * Miolla, A., Cardaioli, M. & Scarpazza, C. Padova emotional dataset of facial

expressions (PEDFE): A unique dataset of genuine and posed emotional facial expressions. _Behavior Research Methods_ 55, 2559–2574, https://doi.org/10.3758/s13428-022-01914-4 (2022). Article

  PubMed  PubMed Central  Google Scholar  * Crosta, A. D. _et al_. The chieti affective action videos database, a resource for the study of emotions in psychology. _Scientific Data_ 7,

https://doi.org/10.1038/s41597-020-0366-1 (2020). * Abadi, M. K. _et al_. Decaf: Meg-based multimodal database for decoding affective physiological responses. _IEEE Transactions on Affective

Computing_ 6, 209–222, https://doi.org/10.1109/TAFFC.2015.2392932 (2015). Article  Google Scholar  * Schaefer, A., Nils, F., Sanchez, X. & Philippot, P. Assessing the effectiveness of a

large database of emotion-eliciting films: A new tool for emotion researchers. _Cognition and Emotion_ 24, 1153–1172 (2010). Article  Google Scholar  * Samson, A. C., Kreibig, S. D.,

Soderstrom, B., Wade, A. A. & Gross, J. J. Eliciting positive, negative and mixed emotional states: A film library for affective scientists. _Cognition and Emotion_ 30, 827–856,

https://doi.org/10.1080/02699931.2015.1031089 (2015). Article  PubMed  Google Scholar  Download references ACKNOWLEDGEMENTS Research reported in this publication was supported by Tiny Blue

Dot Foundation and Joy Ventures. AUTHOR INFORMATION Author notes * These authors contributed equally: Felix Schoeller, Leo Christov Moore. AUTHORS AND AFFILIATIONS * Institute for Advanced

Consciousness Studies, Santa Monica, California, USA Felix Schoeller, Leo Christov Moore, Caite Lynch & Nicco Reggente * Massachusetts Institute of Technology, Media Lab, Cambridge, USA

Felix Schoeller Authors * Felix Schoeller View author publications You can also search for this author inPubMed Google Scholar * Leo Christov Moore View author publications You can also

search for this author inPubMed Google Scholar * Caite Lynch View author publications You can also search for this author inPubMed Google Scholar * Nicco Reggente View author publications

You can also search for this author inPubMed Google Scholar CONTRIBUTIONS F.S., L.C.M., C.L. and N.R. conceptualized the study and designed the experiments. FS and L.C.M. analysed the

results. All authors participated equally in writing the manuscript. CORRESPONDING AUTHORS Correspondence to Felix Schoeller or Leo Christov Moore. ETHICS DECLARATIONS COMPETING INTERESTS In

the past years, FS co-founded and received compensation from BeSound SAS and Nested Minds LLC. ADDITIONAL INFORMATION PUBLISHER’S NOTE Springer Nature remains neutral with regard to

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Differences in Aesthetic Chills Among 2,900+ Southern California Participants. _Sci Data_ 10, 922 (2023). https://doi.org/10.1038/s41597-023-02816-6 Download citation * Received: 21 August

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