The differences in essential facial areas for impressions between humans and deep learning models: An eye‐tracking and explainable AI approach.
Saved in:
| Title: | The differences in essential facial areas for impressions between humans and deep learning models: An eye‐tracking and explainable AI approach. |
|---|---|
| Authors: | Sano, Takanori (AUTHOR), Shi, Jun (AUTHOR), Kawabata, Hideaki (AUTHOR) |
| Source: | British Journal of Psychology. May2026, Vol. 117 Issue 2, p503-527. 25p. |
| Subjects: | Facial anatomy, Pearson correlation (Statistics), Research funding, Eye movement measurements, Artificial intelligence, Masculinity, Femininity, Descriptive statistics, Social dominance, Social attitudes, Attention, Personal beauty, Deep learning, Analysis of variance, Visual perception, Data analysis software, Facial expression, Sexual dimorphism |
| Abstract: | 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. [ABSTRACT FROM AUTHOR] |
| Copyright of British Journal of Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | 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. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 00071269 |
| DOI: | 10.1111/bjop.12744 |
