Multidisciplinary characterization of embarrassment through behavioral and acoustic modeling
Abstract Embarrassment is a social emotion that shares many characteristics with social anxiety (SA). Most people experience embarrassment in their daily lives, but it is quite overlooked in research. We characterized embarrassment through an interdisciplinary approach, introducing a behavioral para...
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| Format: | Article |
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-94051-9 |
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| author | Dajana Šipka Bogdan Vlasenko Maria Stein Thomas Dierks Mathew Magimai-Doss Yosuke Morishima |
| author_facet | Dajana Šipka Bogdan Vlasenko Maria Stein Thomas Dierks Mathew Magimai-Doss Yosuke Morishima |
| author_sort | Dajana Šipka |
| collection | DOAJ |
| description | Abstract Embarrassment is a social emotion that shares many characteristics with social anxiety (SA). Most people experience embarrassment in their daily lives, but it is quite overlooked in research. We characterized embarrassment through an interdisciplinary approach, introducing a behavioral paradigm and applying machine learning approaches, including acoustic analyses. 33 participants wrote about an embarrassing experience and then, without knowing it prior, had to read it out loud to the conductor. Embarrassment was then examined using two different approaches: Firstly, from a subjective view, with self-report measures from the participants. Secondly, from an objective, machine-learning approach, in which trained models tested the robustness of our embarrassment data set (i.e., prediction accuracy), and then described embarrassment in a dimensional (i.e., dimension: valence, arousal, dominance; VAD) and categorical (i.e., comparing embarrassment to other emotional states) way. The subjective rating of embarrassment was increased after participants read their stories out loud, and participants with higher SA scores experienced higher embarrassment than participants with lower SA scores. The state of embarrassment was predicted with 86.4% as the best of the unweighted average recall rates. While the simple VAD dimensional analyses did not differentiate between the state of embarrassment and the references, the complex emotional category analyses characterized embarrassment as closer to boredom, a neutral state, and less of sadness. Combining an effective behavioral paradigm and advanced acoustic modeling, we characterized the emotional state of embarrassment, and the identified characteristics could be used as a biomarker to assess SA. |
| format | Article |
| id | doaj-art-8b5d7675fca64765a4044d7ac0e6b175 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-8b5d7675fca64765a4044d7ac0e6b1752025-08-20T02:32:08ZengNature PortfolioScientific Reports2045-23222025-03-0115111410.1038/s41598-025-94051-9Multidisciplinary characterization of embarrassment through behavioral and acoustic modelingDajana Šipka0Bogdan Vlasenko1Maria Stein2Thomas Dierks3Mathew Magimai-Doss4Yosuke Morishima5Department of Clinical Psychology and Psychotherapy, University of BernIdiap Research InstituteDepartment of Clinical Psychology and Psychotherapy, University of BernUniversity Hospital of Psychiatry and Psychotherapy, University of BernIdiap Research InstituteUniversity Hospital of Psychiatry and Psychotherapy, University of BernAbstract Embarrassment is a social emotion that shares many characteristics with social anxiety (SA). Most people experience embarrassment in their daily lives, but it is quite overlooked in research. We characterized embarrassment through an interdisciplinary approach, introducing a behavioral paradigm and applying machine learning approaches, including acoustic analyses. 33 participants wrote about an embarrassing experience and then, without knowing it prior, had to read it out loud to the conductor. Embarrassment was then examined using two different approaches: Firstly, from a subjective view, with self-report measures from the participants. Secondly, from an objective, machine-learning approach, in which trained models tested the robustness of our embarrassment data set (i.e., prediction accuracy), and then described embarrassment in a dimensional (i.e., dimension: valence, arousal, dominance; VAD) and categorical (i.e., comparing embarrassment to other emotional states) way. The subjective rating of embarrassment was increased after participants read their stories out loud, and participants with higher SA scores experienced higher embarrassment than participants with lower SA scores. The state of embarrassment was predicted with 86.4% as the best of the unweighted average recall rates. While the simple VAD dimensional analyses did not differentiate between the state of embarrassment and the references, the complex emotional category analyses characterized embarrassment as closer to boredom, a neutral state, and less of sadness. Combining an effective behavioral paradigm and advanced acoustic modeling, we characterized the emotional state of embarrassment, and the identified characteristics could be used as a biomarker to assess SA.https://doi.org/10.1038/s41598-025-94051-9 |
| spellingShingle | Dajana Šipka Bogdan Vlasenko Maria Stein Thomas Dierks Mathew Magimai-Doss Yosuke Morishima Multidisciplinary characterization of embarrassment through behavioral and acoustic modeling Scientific Reports |
| title | Multidisciplinary characterization of embarrassment through behavioral and acoustic modeling |
| title_full | Multidisciplinary characterization of embarrassment through behavioral and acoustic modeling |
| title_fullStr | Multidisciplinary characterization of embarrassment through behavioral and acoustic modeling |
| title_full_unstemmed | Multidisciplinary characterization of embarrassment through behavioral and acoustic modeling |
| title_short | Multidisciplinary characterization of embarrassment through behavioral and acoustic modeling |
| title_sort | multidisciplinary characterization of embarrassment through behavioral and acoustic modeling |
| url | https://doi.org/10.1038/s41598-025-94051-9 |
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