ArticleHuman brain mapping2021
Supramodal neural networks support top-down processing of social signals.
Article in Human brain mapping, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- A neurobiological perspective on social influence: Serotonin and social adaptation.Journal of neurochemistry · 2022Review
- Supramodal neural networks support top-down processing of social signals.Human brain mapping · 2021Article
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Authors and funding
7 authors.
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Abstract
The perception of facial and vocal stimuli is driven by sensory input and cognitive top-down influences. Important top-down influences are attentional focus and supramodal social memory representations. The present study investigated the neural networks underlying these top-down processes and their role in social stimulus classification. In a neuroimaging study with 45 healthy participants, we employed a social adaptation of the Implicit Association Test. Attentional focus was modified via the classification task, which compared two domains of social perception (emotion and gender), using the exactly same stimulus set. Supramodal memory representations were addressed via congruency of the target categories for the classification of auditory and visual social stimuli (voices and faces). Functional magnetic resonance imaging identified attention-specific and supramodal networks. Emotion classification networks included bilateral anterior insula, pre-supplementary motor area, and right inferior frontal gyrus. They were pure attention-driven and independent from stimulus modality or congruency of the target concepts. No neural contribution of supramodal memory representations could be revealed for emotion classification. In contrast, gender classification relied on supramodal memory representations in rostral anterior cingulate and ventromedial prefrontal cortices. In summary, different domains of social perception involve different top-down processes which take place in clearly distinguishable neural networks.
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