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  1. Reading Emotion From Mouse Cursor Motions: Affective Computing Approach.Takashi Yamauchi & Kunchen Xiao - 2018 - Cognitive Science 42 (3):771-819.
    Affective computing research has advanced emotion recognition systems using facial expressions, voices, gaits, and physiological signals, yet these methods are often impractical. This study integrates mouse cursor motion analysis into affective computing and investigates the idea that movements of the computer cursor can provide information about emotion of the computer user. We extracted 16–26 trajectory features during a choice-reaching task and examined the link between emotion and cursor motions. Participants were induced for positive or negative emotions by music, film clips, (...)
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  • Discerning Mouse Trajectory Features With the Drift Diffusion Model.Anton Leontyev & Takashi Yamauchi - 2021 - Cognitive Science 45 (10):e13046.
    Mouse tracking, a new action‐based measure of behavior, has advanced theories of decision making with the notion that cognitive and social decision making is fundamentally dynamic. Implicit in this theory is that people's decision strategies, such as discounting delayed rewards, are stable over task design and that mouse trajectory features correspond to specific segments of decision making. By applying the hierarchical drift diffusion model and the Bayesian delay discounting model, we tested these assumptions. Specifically, we investigated the extent to which (...)
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