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      A Multitude of Neural Representations Behind Multisensory “Social Norm” Processing

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          Abstract

          Humans show a unique capacity to process complex information from multiple sources. Social perception in natural environment provides a good example of such capacity as it typically requires the integration of information from different sensory systems, and also from different levels of sensory processing. Here, instead of studying one isolate system and level of representation, we focused upon a neuroimaging paradigm which allows to capture multiple brain representations simultaneously, i.e., low and high-level processing in two different sensory systems, as well as abstract cognitive processing of congruency. Subjects performed social decisions based on the congruency between auditory and visual processing. Using multivoxel pattern analysis (MVPA) of functional magnetic resonance imaging (fMRI) data, we probed a wide variety of representations. Our results confirmed the expected representations at each level and system according to the literature. Further, beyond the hierarchical organization of the visual, auditory and higher order neural systems, we provide a more nuanced picture of the brain functional architecture. Indeed, brain regions of the same neural system show similarity in their representations, but they also share information with regions from other systems. Further, the strength of neural information varied considerably across domains in a way that was not obviously related to task relevance. For instance, selectivity for task-irrelevant animacy of visual input was very strong. The present approach represents a new way to explore the richness of co-activated brain representations underlying the natural complexity in human cognition.

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          Most cited references27

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          Distributed and overlapping representations of faces and objects in ventral temporal cortex.

          The functional architecture of the object vision pathway in the human brain was investigated using functional magnetic resonance imaging to measure patterns of response in ventral temporal cortex while subjects viewed faces, cats, five categories of man-made objects, and nonsense pictures. A distinct pattern of response was found for each stimulus category. The distinctiveness of the response to a given category was not due simply to the regions that responded maximally to that category, because the category being viewed also could be identified on the basis of the pattern of response when those regions were excluded from the analysis. Patterns of response that discriminated among all categories were found even within cortical regions that responded maximally to only one category. These results indicate that the representations of faces and objects in ventral temporal cortex are widely distributed and overlapping.
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            Matching categorical object representations in inferior temporal cortex of man and monkey.

            Inferior temporal (IT) object representations have been intensively studied in monkeys and humans, but representations of the same particular objects have never been compared between the species. Moreover, IT's role in categorization is not well understood. Here, we presented monkeys and humans with the same images of real-world objects and measured the IT response pattern elicited by each image. In order to relate the representations between the species and to computational models, we compare response-pattern dissimilarity matrices. IT response patterns form category clusters, which match between man and monkey. The clusters correspond to animate and inanimate objects; within the animate objects, faces and bodies form subclusters. Within each category, IT distinguishes individual exemplars, and the within-category exemplar similarities also match between the species. Our findings suggest that primate IT across species may host a common code, which combines a categorical and a continuous representation of objects.
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              Identifying natural images from human brain activity.

              A challenging goal in neuroscience is to be able to read out, or decode, mental content from brain activity. Recent functional magnetic resonance imaging (fMRI) studies have decoded orientation, position and object category from activity in visual cortex. However, these studies typically used relatively simple stimuli (for example, gratings) or images drawn from fixed categories (for example, faces, houses), and decoding was based on previous measurements of brain activity evoked by those same stimuli or categories. To overcome these limitations, here we develop a decoding method based on quantitative receptive-field models that characterize the relationship between visual stimuli and fMRI activity in early visual areas. These models describe the tuning of individual voxels for space, orientation and spatial frequency, and are estimated directly from responses evoked by natural images. We show that these receptive-field models make it possible to identify, from a large set of completely novel natural images, which specific image was seen by an observer. Identification is not a mere consequence of the retinotopic organization of visual areas; simpler receptive-field models that describe only spatial tuning yield much poorer identification performance. Our results suggest that it may soon be possible to reconstruct a picture of a person's visual experience from measurements of brain activity alone.
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                Author and article information

                Contributors
                Journal
                Front Hum Neurosci
                Front Hum Neurosci
                Front. Hum. Neurosci.
                Frontiers in Human Neuroscience
                Frontiers Media S.A.
                1662-5161
                23 April 2018
                2018
                : 12
                : 153
                Affiliations
                [1] 1Department of Brain and Cognition, KU Leuven , Leuven, Belgium
                [2] 2Center for Developmental Psychiatry, Department of Neurosciences, KU Leuven , Leuven, Belgium
                [3] 3Leuven Autism Research Consortium, KU Leuven , Leuven, Belgium
                Author notes

                Edited by: Xiaolin Zhou, Peking University, China

                Reviewed by: Shinji Nishimoto, National Institute of Information and Communications Technology, Japan; Edmund C. Lalor, University of Rochester, United States

                *Correspondence: Felipe Pegado felipe.pegado@ 123456kuleuven.be Hans Op de Beeck hans.opdebeeck@ 123456kuleuven.be
                Article
                10.3389/fnhum.2018.00153
                5924771
                8f536dee-17ed-4941-855b-cd923f4e366f
                Copyright © 2018 Pegado, Hendriks, Amelynck, Daniels, Bulthé, Lee Masson, Boets and Op de Beeck.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 11 December 2017
                : 05 April 2018
                Page count
                Figures: 6, Tables: 0, Equations: 0, References: 45, Pages: 14, Words: 9848
                Funding
                Funded by: Fonds Wetenschappelijk Onderzoek 10.13039/501100003130
                Award ID: 12Q4615N, 1528216N, G088216N, 11J2115N
                Funded by: European Research Council 10.13039/501100000781
                Award ID: ERC-2011-Stg-284101
                Categories
                Neuroscience
                Original Research

                Neurosciences
                multisensory,audio-visual,multivoxel pattern analysis,orthogonal design,hierarchical brain,social norm,mentalizing

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