Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, 6200 MD, The Netherlands.
Department of Computer Science, University College London, London, WC1E 6BT, UK.
Sci Rep. 2020 Apr 10;10(1):6202. doi: 10.1038/s41598-020-63125-1.
Humans are experts at recognizing intent and emotion from other people's body movements; however, the underlying mechanisms are poorly understood. Here, we computed quantitative features of body posture and kinematics and acquired behavioural ratings of these feature descriptors to investigate their role in affective whole-body movement perception. Representational similarity analyses and classification regression trees were used to investigate the relation of emotional categories to both the computed features and behavioural ratings. Overall, postural rather than kinematic features discriminated better between emotional movements for the computed as well as for the behavioural features. In particular, limb angles and symmetry appeared to be the most relevant ones. This was observed independently of whether or not the time-related information was preserved in the computed features. Interestingly, the behavioural ratings showed a clearer distinction between affective movements than the computed counterparts. Finally, the perceived directionality of the movement (i.e. towards or away from the observer) was found to be critical for the recognition of fear and anger.
人类是从他人身体动作中识别意图和情感的专家;然而,其潜在机制仍不清楚。在这里,我们计算了身体姿势和运动学的定量特征,并获得了这些特征描述符的行为评分,以研究它们在情感整体运动感知中的作用。代表性相似性分析和分类回归树用于研究情绪类别与计算特征和行为评分的关系。总的来说,姿势特征而不是运动学特征更能区分情绪运动,无论是对于计算特征还是行为特征都是如此。特别是,肢体角度和对称性似乎是最相关的。这一观察结果与计算特征中是否保留了时间相关信息无关。有趣的是,行为评分比计算评分更能清晰地区分情感运动。最后,运动的感知方向(即朝向或远离观察者)对于识别恐惧和愤怒至关重要。