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Character Computing aims to predict human experience and behavior based on cognitive, affective, and motivational states and traits related to a person’s self, personality, mental health, and well-being. Previous Character Computing studies have focused on the analysis of self-report data or behavioral trait and state markers. The present study extends previous Character Computing studies in terms of methods and paradigms by investigating the modulation of event-related brain potentials (ERPs) during self-referential affective processing. Specifically, the study determines whether modulation of event-related brain potentials (ERPs) could differentiate between affective traits and mental states of well-being during processing conditions during which no overt responses or behavior of the participants are provided. A novel, well-controlled experimental paradigm (the so-called affective HisMine paradigm) was chosen. Data from N = 64 healthy subjects were analyzed. The results suggest that ERP modulation during self-referential affective processing could be included in future Character Computing studies and during brain-computer interaction to promote mental health and well-being of healthy users.

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Character Traits and States, Mental Health and Electrophysiological Markers of Self-referential Affective Processing – Novel Approaches from Psychological Research for Character Computing and Brain-Computer Interaction

  • 会议论文

  • 发表日期：

    2025年

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  • Cornelia Herbert

摘要

Character Computing aims to predict human experience and behavior based on cognitive, affective, and motivational states and traits related to a person’s self, personality, mental health, and well-being. Previous Character Computing studies have focused on the analysis of self-report data or behavioral trait and state markers. The present study extends previous Character Computing studies in terms of methods and paradigms by investigating the modulation of event-related brain potentials (ERPs) during self-referential affective processing. Specifically, the study determines whether modulation of event-related brain potentials (ERPs) could differentiate between affective traits and mental states of well-being during processing conditions during which no overt responses or behavior of the participants are provided. A novel, well-controlled experimental paradigm (the so-called affective HisMine paradigm) was chosen. Data from N = 64 healthy subjects were analyzed. The results suggest that ERP modulation during self-referential affective processing could be included in future Character Computing studies and during brain-computer interaction to promote mental health and well-being of healthy users.