国際ジャーナル掲載情報

・Authors: Jumpei Yamashita（山下 純平）， Yoshiaki Takimoto（瀧本 祥章），Haruo Oishi（大石　晴夫），Takatsune Kumada（熊田 孝恒）

・Title: How do personality traits modulate real-world gaze behavior? Generated gaze data shows situation-dependent modulations

・Journal(書誌情報）: Frontiers in Psychology

・doi: https://doi.org/10.3389/fpsyg.2023.1144048

・論文URL: https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2023.1144048/full

・Abstract: It has both scientific and practical benefits to substantiate the theoretical prediction that personality (Big Five) traits systematically modulate gaze behavior in various real-world (working) situations. Nevertheless, previous methods that required controlled situations and large numbers of participants failed to incorporate real-world personality modulation analysis. One cause of this research gap is the mixed effects of individual attributes (e.g., the accumulated attributes of age, gender, and degree of measurement noise) and personality traits in gaze data. Previous studies may have used larger sample sizes to average out the possible concentration of specific individual attributes in some personality traits, and may have imposed control situations to prevent unexpected interactions between these possibly biased individual attributes and complex, realistic situations. Therefore, we generated and analyzed real-world gaze behavior where the effects of personality traits are separated out from individual attributes. In Experiment 1, we successfully provided a methodology for generating such sensor data on head and eye movements for a small sample of participants who performed realistic nonsocial (data-entry) and social (conversation) work tasks (i.e., the first contribution). In Experiment 2, we evaluated the effectiveness of generated gaze behavior for real-world personality modulation analysis. We successfully showed how openness systematically modulates the autocorrelation coefficients of sensor data, reflecting the period of head and eye movements in data-entry and conversation tasks (i.e., the second contribution). We found different openness modulations in the autocorrelation coefficients from the generated sensor data of the two tasks. These modulations could not be detected using real sensor data because of the contamination of individual attributes. In conclusion, our method is a potentially powerful tool for understanding theoretically expected, systematic situation-specific personality modulation of real-world gaze behavior.

・著者Contact先の email:
山下 純平（NTTアクセスサービスシステム研究所）
junpei.yamashita[at]ntt.com（[at]を@に変更して下さい。）

・日本語によるコメント（オプション，200-300字で）:
性格の違う人々の注視行動を分析することで、彼らがいかに異なるやり方で情報を探索しているかを知ることができる可能性があります。しかし、異なる性格ごとの視線データを現実場面で分析しようとしても、計測ノイズなどのランダムな個人差ノイズの影響を受けてしまうことから、簡単には差を見出せない状況でした。本研究では、生成AIの一種であるGANを用いて、性格間の差異は現れるが、ノイズは一定であるような理想的な視線センサデータを生成しました。この理想データを分析することで、ビッグファイブ性格特性のうち経験への開放性が、PC作業や他者との会話など、様々な実場面ごとに注視行動を調節していることが明らかになりました。