Stimulus Inversion And Emotional Expressions Independently Affect Face And Body Perception: An ERP Study

Citation Author(s):
Francesco
Bossi
Department of Information Engineering, University of Pisa, Pisa, Italy
Submitted by:
Francesco Bossi
Last updated:
Mon, 01/15/2024 - 10:42
DOI:
10.21227/2ja6-1z38
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Abstract 

Faces and bodies provide critical cues for social interaction and communication. Their structural encoding depends on configural processing, as suggested by the detrimental effect of stimulus inversion for both faces (i.e., face inversion effect - FIE) and bodies (body inversion effect - BIE). An occipito-temporal negative event-related potential (ERP) component peaking around 170 ms after stimulus onset (N170) is consistently elicited by human faces and bodies and it is affected by the inversion of these stimuli. Albeit it is known that emotional expressions can boost structural encoding (resulting in larger N170 components for emotional than for neutral faces), little is known about body emotional expressions. Thus, the current study investigated the effects of different emotional expressions on structural encoding in combination with FIE and BIE. Three ERP components (P1, N170, P2) were recorded using a 128-channel electroencephalogram (EEG) when participants were presented with (upright and inverted) faces and bodies conveying four possible emotions (happiness, sadness, anger, fear) or no emotion (neutral). Results demonstrated that inversion and emotional expressions independently affected the accuracy and amplitude of all ERP components (P1, N170, P2). In particular, faces showed specific effects of emotional expressions during the structural encoding stage (N170), while P2 amplitude (representing top-down conceptualization) was modified by emotional body perception. Moreover, the task performed by participants (i.e., implicit vs. explicit processing of emotional information) differently influenced accuracy and ERP components. These results support integrated theories of visual perception, thus speaking in favour of the functional independence of the two neurocognitive pathways (one for structural encoding and one for emotional expression analysis) involved in social stimuli processing. Results are discussed highlighting the neurocognitive and computational advantages of the independence between the two pathways.

Instructions: 

CSV datasets used in statistical analyses with mixed-effects linear (LMM) or generalized linear models (GLMM).