Insights into the relationship between eye movements and personality traits in restricted visual fields.

Previous studies have suggested behavioral patterns, such as visual attention and eye movements, relate to individual personality traits. However, these studies mainly focused on free visual tasks, and the impact of visual field restriction remains inadequately understood. The primary objective of this study is to elucidate the patterns of conscious eye movements induced by visual field restriction and to examine how these patterns relate to individual personality traits. Building on previous research, we aim to gain new insights through two behavioral experiments, unraveling the intricate relationship between visual behaviors and individual personality traits. As a result, both Experiment 1 and Experiment 2 revealed differences in eye movements during free observation and visual field restriction. Particularly, simulation results based on the analyzed data showed clear distinctions in eye movements between free observation and visual field restriction conditions. This suggests that eye movements during free observation involve a mixture of conscious and unconscious eye movements. Furthermore, we observed significant correlations between conscious eye movements and personality traits, with more pronounced effects in the visual field restriction condition used in Experiment 2 compared to Experiment 1. These analytical findings provide a novel perspective on human cognitive processes through visual perception.

Conscious observational behavior in recognizing landmarks in facial expressions.

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.