松香 敏彦

Previous studies on facial impression inference have focused on the physical features of faces, with only a few considering the effects of the observer. This study explored how participants’ personality traits directly and indirectly affect the impression inference of human faces. Specifically, we examined how observers’ personality traits impact their eye movements, which in turn influence impression inferences. Experiment 1 found relationships between participants’ personality traits and eye movements, but these did not significantly impact impression inferences. In Experiment 2, we manipulated observers’ observational behavior to control for the potential interactive effect between facial features and participants’ eye movements during impression inference. This manipulation suggested that focusing on different areas of faces leads to different impression inferences. It also suggests that the same person might have different impressions of the exact same face by changing their observational behavior. These results deepen our understanding of the impact of facial features and participants’ personality traits on impression inferences, indicating that observers’ personality traits and observational behavior play a significant role in impression formation.

The present study investigated (1) how well humans can recognize facial expressions represented by a small set of landmarks, a commonly used technique in facial recognition in machine learning and (2) differences in conscious observational behaviors to recognized different types of expressions. Our video stimuli consisted of facial expression represented by 68 landmark points. Conscious observational behaviors were measured by movements of the mouse cursor where a small area around it was only visible to participants. We constructed Bayesian models to analyze how personality traits and observational behaviors influenced how participants recognized different facial expressions. We found that humans could recognize positive expressions with high accuracy, similar to machine learning, even when faces were represented by a small set of landmarks. Although humans fared better than machine learning, recognition of negative expressions was not as high as positives. Our results also showed that personality traits and conscious observational behaviors significantly influenced recognizing facial expressions. For example, people with high agreeableness could correctly recognize faces expressing happiness by observing several areas among faces without focusing on any specific part for very long. These results suggest a mechanism whereby personality traits lead to different conscious observational behaviors and recognitions of facial expressions are based on information obtained through those observational behaviors.

A back-channel is a short utterance made by non-primary speakers. In the Japanese language back-channel is called 'Aizuchi, ' Aizuchi has long been considered as a positive reaction to the primary speaker showing agreement or appraisal and or giving the right to continue speaking. Togashi argues that, in contrast to the conventional view, the essence of aizuchi is a sign of internal information processing in the mind of the one who uses aizuchi. The present study examined the usage of aizuchi as a non-primary speaker's internal information processing using Japanese map task dialogue. The result of our analysis showed that aizuchi often occurred when the primary speakers introduced an object known by the non-primary speakers into the conversation as predicted by Togashi's view of aizuchi. It was also suggested that the presence/absence of aizuchi was not necessarily a clear sign indicating the non-primary speakers have knowledge about the target object but rough but useful information to predict the knowledge state of the non-primary speakers.