The differences in essential facial areas for impressions between humans and deep learning models: An eye‐tracking and explainable AI approach.
This study explored the facial impressions of attractiveness, dominance and sexual dimorphism using experimental and computational methods. In Study 1, we generated face images with manipulated morphological features using geometric morphometrics. In Study 2, we conducted eye tracking and impression...
| Publicado en: | British Journal of Psychology Vol. 117; no. 2; pp. 503 - 528 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Wiley-Blackwell
May2026
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=192785883&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 192785883 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00071269 BJP jtl: British Journal of Psychology issn: 00071269 maglogo: Y pubinfo: dt: May2026 vid: 117 iid: 2 pid: 480 pub: Wiley-Blackwell artinfo: ui: 192785883 10.1111/bjop.12744 ppf: 503 ppct: 25 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P size: 3.1MB tig: atl: The differences in essential facial areas for impressions between humans and deep learning models: An eye‐tracking and explainable AI approach. aug: au: Sano, Takanori Shi, Jun Kawabata, Hideaki affil: Graduate School of Human Relations, Keio University, Minato‐ku Tokyo,, Japan Faculty of Letters, Keio University, Minato‐ku Tokyo,, Japan su: Artificial intelligence Masculinity Femininity Social dominance Social attitudes Attention Personal beauty Analysis of variance Facial expression Facial anatomy Pearson correlation (Statistics) Research funding Eye movement measurements Descriptive statistics Deep learning Visual perception Data analysis software Sexual dimorphism sug: subj: Artificial intelligence Masculinity Femininity Social dominance Social attitudes Attention Personal beauty Analysis of variance Facial expression Facial anatomy Pearson correlation (Statistics) Research funding Eye movement measurements Descriptive statistics Deep learning Visual perception Data analysis software Sexual dimorphism keyword: deep learning explainable AI eye‐tracking facial impressions geometric morphometrics deep learning explainable AI eye‐tracking facial impressions geometric morphometrics ab: This study explored the facial impressions of attractiveness, dominance and sexual dimorphism using experimental and computational methods. In Study 1, we generated face images with manipulated morphological features using geometric morphometrics. In Study 2, we conducted eye tracking and impression evaluation experiments using these images to examine how facial features influence impression evaluations and explored differences based on the sex of the face images and participants. In Study 3, we employed deep learning methods, specifically using gradient‐weighted class activation mapping (Grad‐CAM), an explainable artificial intelligence (AI) technique, to extract important features for each impression using the face images and impression evaluation results from Studies 1 and 2. The findings revealed that eye‐tracking and deep learning use different features as cues. In the eye‐tracking experiments, attention was focused on features such as the eyes, nose and mouth, whereas the deep learning analysis highlighted broader features, including eyebrows and superciliary arches. The computational approach using explainable AI suggests that the determinants of facial impressions can be extracted independently of visual attention. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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