Faces and voices in the brain: A modality-general person-identity representation in superior temporal sulcus
- PMID: 31299368
- DOI: 10.1016/j.neuroimage.2019.07.017
Faces and voices in the brain: A modality-general person-identity representation in superior temporal sulcus
Abstract
Face-selective and voice-selective brain regions have been shown to represent face-identity and voice-identity, respectively. Here we investigated whether there are modality-general person-identity representations in the brain that can be driven by either a face or a voice, and that invariantly represent naturalistically varying face videos and voice recordings of the same identity. Models of face and voice integration suggest that such representations could exist in multimodal brain regions, and in unimodal regions via direct coupling between face- and voice-selective regions. Therefore, in this study we used fMRI to measure brain activity patterns elicited by the faces and voices of familiar people in face-selective, voice-selective, and person-selective multimodal brain regions. We used representational similarity analysis to (1) compare representational geometries (i.e. representational dissimilarity matrices) of face- and voice-elicited identities, and to (2) investigate the degree to which pattern discriminants for pairs of identities generalise from one modality to the other. We did not find any evidence of similar representational geometries across modalities in any of our regions of interest. However, our results showed that pattern discriminants that were trained to discriminate pairs of identities from their faces could also discriminate the respective voices (and vice-versa) in the right posterior superior temporal sulcus (rpSTS). Our findings suggest that the rpSTS is a person-selective multimodal region that shows a modality-general person-identity representation and integrates face and voice identity information.
Keywords: Face recognition; Multisensory processing; Person-identity recognition; Representational similarity analysis; Voice recognition.
Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.
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