The human brain is tuned to recognize emotional facial expressions in faces having a natural upright orientation. The relative contributions of featural, configural, and holistic processing to decision-making are as yet poorly understood. This study used a diffusion decision model (DDM) of decision-making to investigate the contribution of early face-sensitive processes to emotion recognition from physiognomic features (the eyes, nose, and mouth) by determining how experimental conditions tapping those processes affect early face-sensitive neuroelectric reflections (P100, N170, and P250) of processes determining evidence accumulation at the behavioral level. We first examined the effects of both stimulus orientation (upright vs. inverted) and stimulus type (photographs vs. sketches) on behavior and neuroelectric components (amplitude and latency). Then, we explored the sources of variance common to the experimental effects on event-related potentials (ERPs) and the DDM parameters. Several results suggest that the N170 indicates core visual processing for emotion recognition decision-making: (a) the additive effect of stimulus inversion and impoverishment on N170 latency; and (b) multivariate analysis suggesting that N170 neuroelectric activity must be increased to counteract the detrimental effects of face inversion on drift rate and of stimulus impoverishment on the stimulus encoding component of non-decision times. Overall, our results show that emotion recognition is still possible even with degraded stimulation, but at a neurocognitive cost, reflecting the extent to which our brain struggles to accumulate sensory evidence of a given emotion. Accordingly, we theorize that: (a) the P100 neural generator would provide a holistic frame of reference to the face percept through categorical encoding; (b) the N170 neural generator would maintain the structural cohesiveness of the subtle configural variations in facial expressions across our experimental manipulations through coordinate encoding of the facial features; and (c) building on the previous configural processing, the neurons generating the P250 would be responsible for a normalization process adapting to the facial features to match the stimulus to internal representations of emotional expressions.

中文翻译：

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在面部情绪识别过程中与证据积累作斗争的大脑过程：一项 ERP 研究

人脑经过调整以识别具有自然直立方向的面部表情中的情绪面部表情。特征、配置和整体处理对决策的相对贡献尚知之甚少。本研究使用决策的扩散决策模型 (DDM)，通过确定利用这些过程的实验条件如何影响早期面部特征来研究早期面部敏感过程对从面相特征（眼睛、鼻子和嘴巴）进行情绪识别的贡献。决定行为层面证据积累的过程的敏感神经电反射（P100、N170 和 P250）。我们首先检查了刺激方向（直立与倒置）和刺激类型（照片与草图）对行为和神经电组件（振幅和延迟）的影响。然后，我们探索了对事件相关电位 (ERP) 和 DDM 参数的实验影响常见的方差来源。几个结果表明，N170 表示情绪识别决策的核心视觉处理：（a）刺激倒置和贫困对 N170 潜伏期的累加效应；(b) 多变量分析表明必须增加 N170 神经电活动以抵消面部反转对漂移率和刺激贫乏对非决策时间的刺激编码成分的不利影响。总的来说，我们的结果表明，即使刺激减弱，情绪识别仍然是可能的，但需要神经认知成本，这反映了我们的大脑努力积累特定情绪的感官证据的程度。因此，我们认为：(a) P100 神经生成器将通过分类编码为面部感知提供整体参考框架；(b) N170 神经生成器将通过面部特征的坐标编码，在我们的实验操作中保持面部表情细微结构变化的结构凝聚力；(c) 基于先前的配置处理，生成 P250 的神经元将负责适应面部特征的标准化过程，以将刺激与情绪表达的内部表征相匹配。(b) N170 神经生成器将通过面部特征的坐标编码，在我们的实验操作中保持面部表情细微结构变化的结构凝聚力；(c) 基于先前的配置处理，生成 P250 的神经元将负责适应面部特征的标准化过程，以将刺激与情绪表达的内部表征相匹配。(b) N170 神经生成器将通过面部特征的坐标编码，在我们的实验操作中保持面部表情细微结构变化的结构凝聚力；(c) 基于先前的配置处理，生成 P250 的神经元将负责适应面部特征的标准化过程，以将刺激与情绪表达的内部表征相匹配。