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细微的预测动作能揭示行为,无论社会背景如何。

Subtle predictive movements reveal actions regardless of social context.

作者信息

McMahon Emalie G, Zheng Charles Y, Pereira Francisco, Gonzalez Ray, Ungerleider Leslie G, Vaziri-Pashkam Maryam

机构信息

Section on Neurocircuitry, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.

Machine Learning Team, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.

出版信息

J Vis. 2019 Jul 1;19(7):16. doi: 10.1167/19.7.16.

Abstract

Humans have a remarkable ability to predict the actions of others. To address what information enables this prediction and how the information is modulated by social context, we used videos collected during an interactive reaching game. Two participants (an "initiator" and a "responder") sat on either side of a plexiglass screen on which two targets were affixed. The initiator was directed to tap one of the two targets, and the responder had to either beat the initiator to the target (competition) or arrive at the same time (cooperation). In a psychophysics experiment, new observers predicted the direction of the initiators' reach from brief clips, which were clipped relative to when the initiator began reaching. A machine learning classifier performed the same task. Both humans and the classifier were able to determine the direction of movement before the finger lift-off in both social conditions. Further, using an information mapping technique, the relevant information was found to be distributed throughout the body of the initiator in both social conditions. Our results indicate that we reveal our intentions during cooperation, in which communicating the future course of actions is beneficial, and also during competition despite the social motivation to reveal less information.

摘要

人类具有预测他人行为的非凡能力。为了探究何种信息能实现这种预测以及该信息如何受到社会环境的调节,我们使用了在一场互动伸手游戏中收集的视频。两名参与者(一名“发起者”和一名“响应者”)坐在一块安装有两个目标物的有机玻璃屏幕两侧。发起者被指示去点击两个目标物中的一个,而响应者必须要么比发起者先到达目标物(竞争),要么同时到达(合作)。在一项心理物理学实验中,新的观察者从简短的视频片段中预测发起者伸手的方向,这些片段是相对于发起者开始伸手的时间进行剪辑的。一个机器学习分类器执行相同的任务。在两种社会情境下,人类和分类器都能够在手指抬起之前确定运动方向。此外,使用一种信息映射技术,发现在两种社会情境下,相关信息都分布在发起者的全身。我们的结果表明,我们在合作过程中会透露自己的意图,在合作中传达未来的行动过程是有益的,而且在竞争过程中也是如此,尽管从社会动机角度来看透露的信息较少。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c55c/6662941/6ee8e97d9fd5/i1534-7362-19-7-16-f01.jpg

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