Brain Processes While Struggling With Evidence Accumulation During Facial Emotion Recognition: An ERP Study

Abstract

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.

Accounting for endogenous effects in decision-making with a non-linear diffusion decision model

“…We used face sketches as used in Yang et al (2020), which were generated from the Radboud Face Dataset (Langner et al, 2010). Number stimuli were generated at the beginning of the session for each participant, under the constraint that they were 3-digit integers.…”

Images of stylized faces enhance ERP features used for emotion detection

“…Yang et al found that the N170 amplitude increases when there are modified face characteristics that make participants struggle with facial recognition. This indicates extra resource allocation for encoding a face when, for example, blocking certain areas of that face 17 . Liu et al found that Chinese opera masks elicited a reduced N170 amplitude as compared to normal faces 18 .…”

Modulation of Evoked Brain Responses to Biologically and Socially Important Stimuli in Women with Recurrent Depression

“…Следующий за N170 высокоамплитудный компонент P200 имел в нашем исследовании пиковую латентность 210-220 мс и позитивные максимумы в задних отделах. Предполагается, что он отражает процессы различения стимулов и выбор ответа, отмечено влияние на него избирательного внимания и эмоциональности стимула в норме [16,17]. В нашем исследовании изменения в этом компоненте отражены в двух окнах -130-200 мс и 200-300 мс (см.…”