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Multimodal evidence on shape and surface information in individual face processing

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&NA; The significance of shape and surface information for face perception is well established, yet their relative contribution to recognition and their neural underpinnings await clarification. Here, we employ image… Click to show full abstract

&NA; The significance of shape and surface information for face perception is well established, yet their relative contribution to recognition and their neural underpinnings await clarification. Here, we employ image reconstruction to retrieve, assess and visualize such information using behavioral, electroencephalography and functional magnetic resonance imaging data. Our results indicate that both shape and surface information can be successfully recovered from each modality but that the latter is better recovered than the former, consistent with its key role for face representations. Further, shape and surface information exhibit similar spatiotemporal profiles, rely on the extraction of specific visual features, such as eye shape or skin tone, and reveal a systematic representational structure, albeit with more cross‐modal consistency for shape than surface. More generally, the present work illustrates a novel approach to relating and comparing different modalities in terms of perceptual information content. Thus, our results help elucidate the representational basis of individual face recognition while, methodologically, they showcase the utility of image reconstruction and clarify its reliance on diagnostic visual information. HighlightsFace shape and surface information is recovered from behavioral, EEG and fMRI dataSurface information is recovered better than shape from empirical dataShape information is recovered more consistently across modalitiesShape and surface exhibit similar spatiotemporal profiles of neural processingEye shape and skin tone play key roles in individual face representation

Keywords: information; shape; surface information; shape surface; face

Journal Title: NeuroImage
Year Published: 2019

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