Quarmley Megan, Yang Zhibo, Athar Shahrukh, Zelinksy Gregory, Samaras Dimitris, Jarcho Johanna M
Department of Psychology, Temple University, Philadelphia, USA.
Department of Computer Science, Stony Brook University, Stony Brook, USA.
Proc Int Conf Autom Face Gesture Recognit. 2020 Nov;2020:557-561. doi: 10.1109/fg47880.2020.00111. Epub 2021 Jan 18.
Peer-based aggression following social rejection is a costly and prevalent problem for which existing treatments have had little success. This may be because aggression is a complex process influenced by current states of attention and arousal, which are difficult to measure on a moment to moment basis via self report. It is therefore crucial to identify nonverbal behavioral indices of attention and arousal that predict subsequent aggression. We used Support Vector Machines (SVMs) and eye gaze duration and pupillary response features, measured during positive and negative peer-based social interactions, to predict subsequent aggressive behavior towards those same peers. We found that eye gaze and pupillary reactivity not only predicted aggressive behavior, but performed better than models that included information about the participant's exposure to harsh parenting or trait aggression. Eye gaze and pupillary reactivity models also performed equally as well as those that included information about peer reputation (e.g. whether the peer was rejecting or accepting). This is the first study to decode nonverbal eye behavior during social interaction to predict social rejection-elicited aggression.
社交拒绝后基于同伴的攻击行为是一个代价高昂且普遍存在的问题，现有治疗方法对此收效甚微。这可能是因为攻击行为是一个复杂的过程，受当前注意力和唤醒状态的影响，而通过自我报告很难时刻对这些状态进行测量。因此，识别能够预测后续攻击行为的注意力和唤醒的非言语行为指标至关重要。我们使用支持向量机（SVM）以及在基于同伴的积极和消极社交互动过程中测量的注视持续时间和瞳孔反应特征，来预测随后对这些同伴的攻击行为。我们发现，注视和瞳孔反应不仅能预测攻击行为，而且比包含参与者遭受严厉养育或特质攻击信息的模型表现更好。注视和瞳孔反应模型的表现也与包含同伴声誉信息（如同伴是拒绝还是接受）的模型相当。这是第一项在社交互动过程中解码非言语眼部行为以预测社交拒绝引发的攻击行为的研究。
