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Poster B37

Happy and angry facial expressions are processed independently of task demands and context congruency – An ERP Mass Univariate Analysis

Poster Session B - Sunday, April 14, 2024, 8:00 – 10:00 am EDT, Sheraton Hall ABC

Amie Durston1 (, Calla Mueller1, Roxane Itier1; 1University of Waterloo

Neural decoding of others’ facial expressions is a critical first step in social interactions. Whether this decoding is influenced by task demands and by context remains debated. A previous study from our lab investigated these potential modulations using event related potentials (ERPs). We presented neutral faces paired with negative or positive situational sentences, followed by the same individuals’ faces expressing happiness or anger, as if reacting to the situation in a congruent or incongruent way. In this within-subjects design, participants discriminated between the two expressions (emotion task) and identified if the situation and emotional expression matched (congruency task). The original publication analyzed ERP data following expression onset with a classic approach, focusing on specific electrodes and time points, an approach known to inflate type I and type II statistical errors. The present project re-analyzed these data across the entire epoch and scalp using LIMO EEG, a robust hierarchical Mass Univariate Analysis toolbox. We found significant effects of expression during the N170-P2 interval (113-234ms), and a main effect of congruency around a P3 or LPP-like component (236-398ms). Congruency interacted with task, being significant in the congruency task only, suggesting a limited and task-dependent influence of semantic context. Importantly, emotion did not interact with any factor, suggesting facial expressions were decoded automatically, regardless of context or task demands. The results and their discrepancies with the original findings will be discussed in the context of ERP statistics and the replication crisis.

Topic Area: EMOTION & SOCIAL: Person perception


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April 13–16  |  2024