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European Journal of NeuroscienceNeurosciencePeer-reviewed journal article

Spatio-Temporal Dynamics of Oscillatory Brain Activity during the Observation of Actions and Interactions between Point-light Agents

Elisabeth V. C. FriedrichiD, Imme C. Zillekens, Anna Lena BieliD, Dariusz O’LearyiD, Johannes SingeriD, Eva Victoria Seegenschmiedt, Paul SausengiD, Leonhard SchilbachiD

PDFDOIPreprint

Abstract

Predicting actions from nonverbal cues and using them to optimize ones response behavior (i.e., interpersonal predictive coding) is essential in everyday social interactions. We aimed to investigate the neural correlates of different cognitive processes evolving over time during interpersonal predictive coding. Thirty-nine participants watched two agents depicted by moving point-light stimuli while an electroencephalogram (EEG) was recorded. One well-recognizable agent performed either a communicative or an individual action. The second agent either was blended into a cluster of noise dots (i.e., present), or was entirely replaced by noise dots (i.e., absent), which participants had to differentiate. EEG amplitude and coherence analyses for theta, alpha and beta frequency bands revealed a dynamic pattern unfolding over time: Watching communicative actions was associated with enhanced coupling within medial anterior regions involved in social and mentalizing processes and with dorsolateral prefrontal activation indicating a higher deployment of cognitive resources. Trying to detect the agent in the cluster of noise dots without having seen communicative cues was related to enhanced coupling in posterior regions for social perception and visual processing. Observing an expected outcome was modulated by motor system activation. Finally, when the agent was detected correctly, activation in posterior areas for visual processing of socially-relevant features was increased. Taken together, our results demonstrate that it is crucial to consider the temporal dynamics of social interactions and of their neural correlates to better understand interpersonal predictive coding. This could lead to optimized treatment approaches for individuals with problems in social interactions.

Citations
2
FWCI
0.47
Field percentile
top 32%

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