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面部表情对虚拟环境中的会话结果的贡献大于身体动作。

Facial expressions contribute more than body movements to conversational outcomes in avatar-mediated virtual environments.

机构信息

Virtual Human Interaction Lab, Department of Communication, Stanford University, 450 Serra Mall, Stanford, CA, 94305, USA.

出版信息

Sci Rep. 2020 Nov 26;10(1):20626. doi: 10.1038/s41598-020-76672-4.

Abstract

This study focuses on the individual and joint contributions of two nonverbal channels (i.e., face and upper body) in avatar mediated-virtual environments. 140 dyads were randomly assigned to communicate with each other via platforms that differentially activated or deactivated facial and bodily nonverbal cues. The availability of facial expressions had a positive effect on interpersonal outcomes. More specifically, dyads that were able to see their partner's facial movements mapped onto their avatars liked each other more, formed more accurate impressions about their partners, and described their interaction experiences more positively compared to those unable to see facial movements. However, the latter was only true when their partner's bodily gestures were also available and not when only facial movements were available. Dyads showed greater nonverbal synchrony when they could see their partner's bodily and facial movements. This study also employed machine learning to explore whether nonverbal cues could predict interpersonal attraction. These classifiers predicted high and low interpersonal attraction at an accuracy rate of 65%. These findings highlight the relative significance of facial cues compared to bodily cues on interpersonal outcomes in virtual environments and lend insight into the potential of automatically tracked nonverbal cues to predict interpersonal attitudes.

摘要

本研究关注两个非言语渠道(即面部和上半身)在虚拟环境中的个体和联合贡献。140 对被试随机分配通过平台进行交流,这些平台分别激活或去激活面部和身体非言语线索。面部表情的可用性对人际关系结果有积极影响。更具体地说,能够看到伴侣面部动作映射到他们的虚拟形象上的对更喜欢彼此,对伴侣的印象更准确,并更积极地描述他们的互动体验,而那些无法看到面部动作的对则不然。然而,只有当伴侣的身体姿势也可用时,这才是正确的,而不是只有面部动作可用时。当对能够看到他们伴侣的身体和面部动作时,他们表现出更大的非言语同步性。本研究还采用机器学习来探索非言语线索是否可以预测人际吸引力。这些分类器以 65%的准确率预测了高和低人际吸引力。这些发现突出了面部线索相对于身体线索在虚拟环境中对人际关系结果的相对重要性,并为自动跟踪的非言语线索预测人际关系态度的潜力提供了见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a73/7692542/2275c404fce1/41598_2020_76672_Fig1_HTML.jpg

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