Space-by-time manifold representation of dynamic facial expressions for emotion categorization.
Space-by-time manifold representation of dynamic facial expressions for emotion categorization.
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DOI：
10.1167/16.8.14
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发表时间：
2016-06-01
影响因子：
1.8
通讯作者：
Schyns PG
中科院分区：
文献类型：
--
作者：
Delis I;Chen C;Jack RE;Garrod OG;Panzeri S;Schyns PG
视觉分类是一种大脑计算，它将视觉环境中的高维信息简化为一组更小的有意义的类别。视觉神经科学中的一个重要问题是识别大脑必须代表的视觉信息，然后用来对视觉输入进行分类。在这里，我们引入了一种新的数学形式--称为时空流形分解--它将这些信息描述为在空间和时间上可分离的低维流形。我们使用这种分解来描述观察者用来对六种典型的情绪面部表情(高兴、惊讶、恐惧、厌恶、愤怒和悲伤)进行分类的表征。通过生成面孔语法，我们在每个实验中呈现了随机的动态脸部动作，并使用主观人类感知来识别与每种情绪类别相关的脸部动作。当随机运动投射到与其中一个情绪类别对应的分类流形区域时，观察者相应地对刺激进行分类；否则，他们选择“其他”。利用这些信息，我们确定了动作单元和时间成分，它们的线性组合导致了对每种情绪的可靠分类。在验证性实验中，我们证实了由此产生的时空流形表征的心理有效性。最后，我们证明了时间序列对于准确的情绪分类的重要性，并确定了导致特定情绪(例如，恐惧和惊讶)以及那些解决这些困惑的典型情绪之间的动作单元成分的时间动力学。
Visual categorization is the brain computation that reduces high-dimensional information in the visual environment into a smaller set of meaningful categories. An important problem in visual neuroscience is to identify the visual information that the brain must represent and then use to categorize visual inputs. Here we introduce a new mathematical formalism—termed space-by-time manifold decomposition—that describes this information as a low-dimensional manifold separable in space and time. We use this decomposition to characterize the representations used by observers to categorize the six classic facial expressions of emotion (happy, surprise, fear, disgust, anger, and sad). By means of a Generative Face Grammar, we presented random dynamic facial movements on each experimental trial and used subjective human perception to identify the facial movements that correlate with each emotion category. When the random movements projected onto the categorization manifold region corresponding to one of the emotion categories, observers categorized the stimulus accordingly; otherwise they selected “other.” Using this information, we determined both the Action Unit and temporal components whose linear combinations lead to reliable categorization of each emotion. In a validation experiment, we confirmed the psychological validity of the resulting space-by-time manifold representation. Finally, we demonstrated the importance of temporal sequencing for accurate emotion categorization and identified the temporal dynamics of Action Unit components that cause typical confusions between specific emotions (e.g., fear and surprise) as well as those resolving these confusions.