When the Timing is Right: The Link Between Temporal Coupling in Dyadic Interactions and Emotion Recognition

JOURNAL OF SPORT & EXERCISE PSYCHOLOGY(2021)

引用 2|浏览0
暂无评分
摘要
Affective states can be understood as dynamic interpersonal processes developing over time and space. When we observe emotional interactions performed by other individuals, our visual system anticipates how the action will unfold. Thus, it has been proposed that the process of emotion perception is not only a simulative but also a predictive process - a phenomenon described as interpersonal predictive coding. The present study investigated whether the recognition of emotions from dyadic interactions depends on a fixed spatiotemporal coupling of the agents. We used an emotion recognition task to manipulate the actions of two interacting point-light figures by implementing different temporal offsets that delayed the onset of one of the agent's actions (+0 ms, +500 ms, +1000 ms or + 2000 ms). Participants had to determine both the subjective valence and the emotion category (happiness, anger, sadness, affection) of the interaction. Results showed that temporal decoupling had a critical effect on both emotion recognition and the subjective impression of valence intensity: Both measures decreased with increasing temporal offset. However, these effects were dependent on which emotion was displayed. Whereas affection and anger sequences were impacted by the temporal manipulation, happiness and sadness were not. To further investigate these effects, we conducted post-hoc exploratory analyses of interpersonal movement parameters. Our findings complement and extend previous evidence by showing that the complex, noncoincidental coordination of actions within dyadic interactions results in a meaningful movement pattern and might serve as a fundamental factor in both detecting and understanding complex actions during human interaction.
更多
查看译文
关键词
Emotion recognition,Dyadic interaction,Temporal coupling,Point -light displays,Interpersonal predictive coding,Simulation theory
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要