https://scholars.lib.ntu.edu.tw/handle/123456789/625561
標題: | Beauty-in-averageness and its contextual modulations: A Bayesian statistical account | 作者: | Ryali C.K ANGELA YU-CHEN LIN |
公開日期: | 2018 | 卷: | 2018-December | 起(迄)頁: | 4082-4092 | 來源出版物: | Advances in Neural Information Processing Systems | 摘要: | 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 statistical typicality is high, in consonance with Barlow's seminal “efficient 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. © 2018 Curran Associates Inc..All rights reserved. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064821007&partnerID=40&md5=ace954c0fbd77da7e63e9cce3def17f5 https://scholars.lib.ntu.edu.tw/handle/123456789/625561 |
ISSN: | 10495258 | SDG/關鍵字: | Encoding (symbols); Signal encoding; Cognitive science; Context dependent; Contextual modulation; Face representations; Facial attractiveness; Model prediction; Negative affects; Statistical coding; Statistics |
顯示於: | 環境工程學研究所 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。