Emotion or expressivity? An automated analysis of nonverbal perception in a social dilemma
Emotion or expressivity? An automated analysis of nonverbal perception in a social dilemma
复制标题
情感还是表现力？
DOI：
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
J. Gratch
中科院分区：
文献类型：
--
作者：
Su Lei;Kalin Stefanov;J. Gratch
大量的研究已经研究了特定的情感表达如何塑造社会感知和社会决策，但最近的情感研究学术界对情感作为一种建构的有效性提出了质疑。在这篇文章中，我们将测量情感表达的价值与更一般的表达性结构（通过任何非语言行为传达思想或情感的意义）进行了对比，并开发了可以自动从视频中提取感知表达性的模型。虽然不太广泛，但大量研究表明，在研究人际知觉时，表达力是一个重要因素，特别是在精神病背景下。在这里，我们研究的作用，表现力在预测社会的看法和决策的背景下，社会困境。我们发现，知觉者在判断表达能力时使用的不仅仅是面部表情，而且将这些表情视为传达思想和情感（尽管面部表情和情感归因解释了这些判断中的大部分差异）。接下来，我们将展示使用Lasso和随机森林可以高精度地预测表达力。我们的分析表明，运动动力学相关的功能是特别重要的建模这些判断。我们还表明，学习模型的表达能力有价值的认识社会情况的重要方面。首先，我们回顾了以前发表的一项发现，该发现表明微笑强度与社交困境中的意外结果有关;相反，我们表明表达能力是这一发现的更好预测（和解释）。其次，我们提供了初步的证据，表现力是有用的识别“感兴趣的时刻”的视频序列。
An extensive body of research has examined how specific emotional expressions shape social perceptions and social decisions, yet recent scholarship in emotion research has raised questions about the validity of emotion as a construct. In this article, we contrast the value of measuring emotional expressions with the more general construct of expressivity (in the sense of conveying a thought or emotion through any nonverbal behavior) and develop models that can automatically extract perceived expressivity from videos. Although less extensive, a solid body of research has shown expressivity to be an important element when studying interpersonal perception, particularly in psychiatric contexts. Here we examine the role expressivity plays in predicting social perceptions and decisions in the context of a social dilemma. We show that perceivers use more than facial expressions when making judgments of expressivity and see these expressions as conveying thoughts as well as emotions (although facial expressions and emotional attributions explain most of the variance in these judgments). We next show that expressivity can be predicted with high accuracy using Lasso and random forests. Our analysis shows that features related to motion dynamics are particularly important for modeling these judgments. We also show that learned models of expressivity have value in recognizing important aspects of a social situation. First, we revisit a previously published finding which showed that smile intensity was associated with the unexpectedness of outcomes in social dilemmas; instead, we show that expressivity is a better predictor (and explanation) of this finding. Second, we provide preliminary evidence that expressivity is useful for identifying “moments of interest” in a video sequence.
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影响因子：
7.6
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DOI：
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发表时间：
2020
期刊：
Proceedings of the 3rd Workshop of Affective Content Analysis
影响因子：
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DOI：
10.1080/09638280410001663030
发表时间：
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期刊：
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影响因子：
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作者：
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