Ryali, Chaitanya ; Yu, Angela J (2018)
Beauty-in-averageness and its contextual modulations: A Bayesian statistical account.
2018 Conference on Neural Information Processing Systems (NeurIPS 2018). Montreal (03.12.2018-08.12.2018)
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Understanding how humans perceive the likability of high-dimensional objects'' such as faces is an important problem in both cognitive science and AI/ML. Existing models generally assume these preferences to be fixed. However, psychologists have found human assessment of facial attractiveness to be context-dependent. Specifically, the classical Beauty-in-Averageness (BiA) effect, whereby a blended face is judged to be more attractive than the originals, is significantly diminished or reversed when the original faces are recognizable, or when the blend is mixed-race/mixed-gender and the attractiveness judgment is preceded by a race/gender categorization, respectively. This "Ugliness-in-Averageness" (UiA) effect has previously been explained via a qualitative disfluency account, which posits that the negative affect associated with the difficult race or gender categorization is inadvertently interpreted by the brain as a dislike for the face itself. In contrast, we hypothesize that human preference for an object is increased when it incurs lower encoding cost, in particular when its perceived {\textbackslashit statistical typicality} is high, in consonance with Barlow's seminalefficient coding hypothesis.'' This statistical coding cost account explains both BiA, where facial blends generally have higher likelihood than ``parent faces'', and UiA, when the preceding context or task restricts face representation to a task-relevant subset of features, thus redefining statistical typicality and encoding cost within that subspace. We use simulations to show that our model provides a parsimonious, statistically grounded, and quantitative account of both BiA and UiA. We validate our model using experimental data from a gender categorization task. We also propose a novel experiment, based on model predictions, that will be able to arbitrate between the disfluency account and our statistical coding cost account of attractiveness.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2018 |
| Autor(en): | Ryali, Chaitanya ; Yu, Angela J |
| Art des Eintrags: | Bibliographie |
| Titel: | Beauty-in-averageness and its contextual modulations: A Bayesian statistical account |
| Sprache: | Englisch |
| Publikationsjahr: | 2018 |
| Ort: | Red Hook, NY |
| Verlag: | Curran Associates, Inc. |
| Buchtitel: | 32nd Conference on Neural Information Processing Systems (NeurIPS 2018) |
| Band einer Reihe: | 31 |
| Veranstaltungstitel: | 2018 Conference on Neural Information Processing Systems (NeurIPS 2018) |
| Veranstaltungsort: | Montreal |
| Veranstaltungsdatum: | 03.12.2018-08.12.2018 |
| URL / URN: | https://proceedings.neurips.cc/paper/2018/hash/84ddfb34126fc... |
| Kurzbeschreibung (Abstract): | Understanding how humans perceive the likability of high-dimensional objects'' such as faces is an important problem in both cognitive science and AI/ML. Existing models generally assume these preferences to be fixed. However, psychologists have found human assessment of facial attractiveness to be context-dependent. Specifically, the classical Beauty-in-Averageness (BiA) effect, whereby a blended face is judged to be more attractive than the originals, is significantly diminished or reversed when the original faces are recognizable, or when the blend is mixed-race/mixed-gender and the attractiveness judgment is preceded by a race/gender categorization, respectively. This "Ugliness-in-Averageness" (UiA) effect has previously been explained via a qualitative disfluency account, which posits that the negative affect associated with the difficult race or gender categorization is inadvertently interpreted by the brain as a dislike for the face itself. In contrast, we hypothesize that human preference for an object is increased when it incurs lower encoding cost, in particular when its perceived {\textbackslashit statistical typicality} is high, in consonance with Barlow's seminalefficient coding hypothesis.'' This statistical coding cost account explains both BiA, where facial blends generally have higher likelihood than ``parent faces'', and UiA, when the preceding context or task restricts face representation to a task-relevant subset of features, thus redefining statistical typicality and encoding cost within that subspace. We use simulations to show that our model provides a parsimonious, statistically grounded, and quantitative account of both BiA and UiA. We validate our model using experimental data from a gender categorization task. We also propose a novel experiment, based on model predictions, that will be able to arbitrate between the disfluency account and our statistical coding cost account of attractiveness. |
| Fachbereich(e)/-gebiet(e): | 03 Fachbereich Humanwissenschaften 03 Fachbereich Humanwissenschaften > Institut für Psychologie |
| Hinterlegungsdatum: | 27 Okt 2023 13:42 |
| Letzte Änderung: | 30 Okt 2023 06:34 |
| PPN: | 512755167 |
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