Department of Psychology, University of California, Berkeley, CA, USA.
Vision Science Program, University of California, Berkeley, CA, USA.
J Vis. 2023 Mar 1;23(3):12. doi: 10.1167/jov.23.3.12.
A critical function of the human visual system is to track emotion accurately and continuously. However, visual information about emotion fluctuates over time. Ideally, the visual system should track these temporal fluctuations-these "natural emotion statistics" of the world-over time. This would balance the need to detect changes in emotion with the need to maintain the stability of visual scene representations. The visual system could promote this goal through serial dependence, which biases our perception of facial expressions toward those seen in the recent past and thus smooths our perception of the world. Here, we quantified the natural emotion statistics in videos by measuring the autocorrelations in emotional content present in films and movies. The results showed that observers' perception of emotion was smoothed over ∼12 seconds or more, and this time-course closely followed the temporal fluctuations in visual information about emotion found in natural scenes. Moreover, the temporal and feature tuning of the perceptual smoothing was consistent with known properties of serial dependence. Our findings suggest that serial dependence is introduced in the perception of emotion to match the natural autocorrelations that are observed in the real world, an operation that could improve the efficiency, sensitivity, and stability of emotion perception.
人类视觉系统的一个关键功能是准确且持续地跟踪情绪。然而，有关情绪的视觉信息会随时间波动。理想情况下，视觉系统应该随时间跟踪这些时间波动——这些“世界的自然情绪统计数据”。这将平衡检测情绪变化的需求与保持视觉场景表示稳定的需求。视觉系统可以通过序列依赖来促进这一目标，序列依赖会使我们对面部表情的感知偏向于最近看到的表情，从而使我们对世界的感知更加平滑。在这里，我们通过测量电影和视频中存在的情感内容的自相关来量化自然情绪统计数据。结果表明，观察者对情绪的感知在 12 秒或更长时间内变得平滑，并且这个时间过程与在自然场景中发现的有关情绪的视觉信息的时间波动密切相关。此外，感知平滑的时间和特征调整与序列依赖的已知属性一致。我们的研究结果表明，序列依赖被引入到对情绪的感知中，以匹配在现实世界中观察到的自然自相关，这一操作可以提高情绪感知的效率、灵敏度和稳定性。
