Neural network modeling of predictive learning capturing temporal hierarchical structure

2023 Fiscal Year Final Research Report

• Project/Area Number: 19K14471
• Research Category: Grant-in-Aid for Early-Career Scientists
• Allocation Type: Multi-year Fund
• Review Section: Basic Section 10040:Experimental psychology-related
• Research Institution: Kyoto University
• Principal Investigator: Nakayama Masataka 京都大学, 人と社会の未来研究院, 特定講師 (40838398)
• Project Period (FY): 2019-04-01 – 2024-03-31
• Keywords: 感情 / 畏怖・畏敬 / 予測 / 災害 / 幸福感 / 感動

Outline of Final Research Achievements

In this study, we used predictive learning with neural network models to examine the learning of temporal structures. In particular, we had planned to collect data on the emotion of awe that is elicited when encountering grand situations that cannot be predicted. Significant results were expected from the data on awe, and the importance of awe emotions increased in the face of the unpredictable threat of the COVID-19 pandemic. We conducted research focusing on awe emotions. As a result, awe emotions were shown to be ambivalent emotions with both positive and negative aspects, and their ambivalence was particularly strong in Japan compared to the United States. It was also suggested that negative emotions in this context could enhance happiness by re-recognizing the value of ordinariness. These results have been published in papers and other forms.

Free Research Field

実験心理学

Academic Significance and Societal Importance of the Research Achievements

今日の社会は、コロナ禍や地球温暖化など、人類が地球規模で対処すべきかつ今まで経験したことがないような脅威に晒されている。このような脅威に対して人々はどのような感情を抱き、どのような行動を起こすのか。これを理解することは地球規模で協力して実際に脅威に対処するために必要である。本研究はawe感情に焦点をあて、その基礎的な性質、および帰結について体系的に検討した。特に文化差の研究では、同じ対象に対するawe感情であっても米国では日本とを比べてポジティブであり、日本ではネガティブであることを示した。このような違いを理解することは地球規模での脅威への対処の協力に向けて重要な知見を提供する。