The temporal and spatial neural processing of faces has been investigated rigorously, but few studies have unified these dimensions to reveal the spatio-temporal dynamics postulated by the models of face processing. We used support vector machine decoding and representational similarity analysis to combine information from different locations (fMRI), time windows (EEG), and theoretical models. By correlating representational dissimilarity matrices (RDMs) derived from multiple pairwise classifications of neural responses to different facial expressions (neutral, happy, fearful, angry), we found early EEG time windows (starting around 130 ms) to match fMRI data from primary visual cortex (V1), and later time windows (starting around 190 ms) to match data from lateral occipital, fusiform face complex, and temporal-parietal-occipital junction (TPOJ). According to model comparisons, the EEG classification results were based more on low-level visual features than expression intensities or categories. In fMRI, the model comparisons revealed change along the processing hierarchy, from low-level visual feature coding in V1 to coding of intensity of expressions in the right TPOJ. The results highlight the importance of a multimodal approach for understanding the functional roles of different brain regions in face processing.

中文翻译：

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人脸感知的时空动态

人脸的时空神经处理已被严格研究，但很少有研究统一这些维度来揭示人脸处理模型所假设的时空动态。我们使用支持向量机解码和表征相似性分析来组合来自不同位置 (fMRI)、时间窗口 (EEG) 和理论模型的信息。通过关联来自对不同面部表情（中性、快乐、恐惧、愤怒）的神经反应的多个成对分类的表征差异矩阵 (RDM)，我们发现早期 EEG 时间窗口（从 130 毫秒左右开始）以匹配来自初级视觉皮层的 fMRI 数据(V1) 和稍后的时间窗口（从 190 毫秒左右开始）以匹配来自外侧枕骨、梭形面部复合体和颞顶枕骨交界处 (TPOJ) 的数据。根据模型比较，EEG 分类结果更多地基于低级视觉特征，而不是表达强度或类别。在 fMRI 中，模型比较揭示了沿处理层次结构的变化，从 V1 中的低级视觉特征编码到右侧 TPOJ 中表达强度的编码。结果强调了多模式方法对于理解不同大脑区域在面部处理中的功能作用的重要性。