The ability to accurately judge another person's emotional states with a short duration of observations is a unique perceptual mechanism of humans, termed as the thin-sliced judgment. In this work, we propose a computational framework based on mutual information to identify the thin-sliced emotion-rich behavior segments within each session and further use these segments to train the session-level affect regressors. Our proposed thin-sliced framework obtains regression accuracies measured in Spearman correlations of 0.605, 0.633, and 0.672 on session-level attributes of activation, dominance, and valence, respectively. It outperforms framework using data of the entire session as baseline. The significant improvement in the regression correlations reinforces the thin-sliced nature of human emotion perception. By properly extracting these emotion-rich behavior segments, we obtain not only an improved overall accuracy but also bring additional insights. Specifically, our detailed analyses indicate that this thin-sliced nature in emotion perception is more evident for attributes of activation and valence, and the within-session time distribution of emotion-salient behavior is located more toward the ending portion. Lastly, we observe that there indeed exists a certain set of behavior types that carry high emotion-related content, and this is especially apparent in the extreme emotion levels.

中文翻译：

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会话级影响感知中细化行为段的计算分析

通过短时间的观察准确判断另一个人的情绪状态的能力是人类独特的感知机制，称为薄切片判断。在这项工作中，我们提出了一个基于互信息的计算框架来识别每个会话中的薄切片情感丰富的行为片段，并进一步使用这些片段来训练会话级影响回归量。我们提出的薄切片框架在激活、优势和效价的会话级属性上分别获得了以 0.605、0.633 和 0.672 的 Spearman 相关性测量的回归准确度。它优于使用整个会话的数据作为基线的框架。回归相关性的显着改善加强了人类情感感知的细化性质。通过正确提取这些情感丰富的行为片段，我们不仅获得了更高的整体准确性，而且还带来了额外的见解。具体来说，我们的详细分析表明，情绪感知的这种细化性质对于激活和效价的属性更为明显，并且情绪显着行为的会话内时间分布更靠近结束部分。最后，我们观察到确实存在一些带有高情绪相关内容的行为类型，这在极端情绪水平上尤为明显。情绪显着行为的会话内时间分布更趋向于结束部分。最后，我们观察到确实存在一些带有高情绪相关内容的行为类型，这在极端情绪水平上尤为明显。情绪显着行为的会话内时间分布更趋向于结束部分。最后，我们观察到确实存在一些带有高情绪相关内容的行为类型，这在极端情绪水平上尤为明显。