Attention Perception & PsychophysicsNeurosciencePeer-reviewed journal article
(Micro)saccade-related potentials during face recognition: A study combining EEG, eye-tracking, and deconvolution modeling
Abstract
Under natural viewing conditions, complex stimuli such as human faces are typically looked at several times in succession, implying that their recognition may unfold across multiple eye fixations. Although electrophysiological (EEG) experiments on face recognition typically prohibit eye movements, participants still execute frequent (micro)saccades on the face, each of which generates its own visuocortical response. This finding raises the question of whether the fixation-related potentials (FRPs) evoked by these tiny gaze shifts also contain psychologically valuable information about face processing. Here we investigated this question by co-recording EEG and eye movements in an experiment with emotional faces (happy, angry, neutral). Deconvolution modeling was used to separate the stimulus-ERPs to face onset from the FRPs generated by subsequent microsaccades-induced refixations on the face. As expected, stimulus-ERPs exhibited typical emotion effects, with a larger early posterior negativity (EPN) for happy/angry compared to neutral faces. Eye-tracking confirmed that participants made small saccades within the face in 98% of the trials. However, while each saccade produced a strong response over visual areas, this response was unaffected by the faces emotional expression, both for the first and for subsequent (micro)saccades. This finding suggests that the faces affective content is rapidly evaluated after stimulus onset, leading to only a short-lived sensory enhancement by arousing stimuli that does not repeat itself during immediate refixations. Methodologically, our work demonstrates how eye-tracking and deconvolution modeling can be used to extract several brain responses from each EEG trial, providing insights into neural processing at different latencies after stimulus onset.
- organism: human
- assay: electronic health records
- research focus: cell-cell communication
- research focus: differential expression
- research focus: spatial domain detection
- approach: deep learning
- approach: statistical model
- Citations
- 11
- FWCI
- 2.67
- Field percentile
- top 11%
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