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      Representational geometry: integrating cognition, computation, and the brain

      review-article
        1 ,   1 , 2
      Trends in Cognitive Sciences
      Elsevier Science

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          Highlights

          • Representational geometry is a framework that enables us to relate brain, computation, and cognition.

          • Representations in brains and models can be characterized by representational distance matrices.

          • Distance matrices can be readily compared to test computational models.

          • We review recent insights into perception, cognition, memory, and action and discuss current challenges.

          Abstract

          The cognitive concept of representation plays a key role in theories of brain information processing. However, linking neuronal activity to representational content and cognitive theory remains challenging. Recent studies have characterized the representational geometry of neural population codes by means of representational distance matrices, enabling researchers to compare representations across stages of processing and to test cognitive and computational theories. Representational geometry provides a useful intermediate level of description, capturing both the information represented in a neuronal population code and the format in which it is represented. We review recent insights gained with this approach in perception, memory, cognition, and action. Analyses of representational geometry can compare representations between models and the brain, and promise to explain brain computation as transformation of representational similarity structure.

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          Most cited references93

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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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            A cortical representation of the local visual environment.

            Medial temporal brain regions such as the hippocampal formation and parahippocampal cortex have been generally implicated in navigation and visual memory. However, the specific function of each of these regions is not yet clear. Here we present evidence that a particular area within human parahippocampal cortex is involved in a critical component of navigation: perceiving the local visual environment. This region, which we name the 'parahippocampal place area' (PPA), responds selectively and automatically in functional magnetic resonance imaging (fMRI) to passively viewed scenes, but only weakly to single objects and not at all to faces. The critical factor for this activation appears to be the presence in the stimulus of information about the layout of local space. The response in the PPA to scenes with spatial layout but no discrete objects (empty rooms) is as strong as the response to complex meaningful scenes containing multiple objects (the same rooms furnished) and over twice as strong as the response to arrays of multiple objects without three-dimensional spatial context (the furniture from these rooms on a blank background). This response is reduced if the surfaces in the scene are rearranged so that they no longer define a coherent space. We propose that the PPA represents places by encoding the geometry of the local environment.
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              Information-based functional brain mapping.

              The development of high-resolution neuroimaging and multielectrode electrophysiological recording provides neuroscientists with huge amounts of multivariate data. The complexity of the data creates a need for statistical summary, but the local averaging standardly applied to this end may obscure the effects of greatest neuroscientific interest. In neuroimaging, for example, brain mapping analysis has focused on the discovery of activation, i.e., of extended brain regions whose average activity changes across experimental conditions. Here we propose to ask a more general question of the data: Where in the brain does the activity pattern contain information about the experimental condition? To address this question, we propose scanning the imaged volume with a "searchlight," whose contents are analyzed multivariately at each location in the brain.
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                Author and article information

                Contributors
                Journal
                Trends Cogn Sci
                Trends Cogn. Sci. (Regul. Ed.)
                Trends in Cognitive Sciences
                Elsevier Science
                1364-6613
                1879-307X
                1 August 2013
                August 2013
                : 17
                : 8
                : 401-412
                Affiliations
                [0005]Medical Research Council, Cognition and Brain Sciences Unit, Cambridge, UK
                [0010]Department of Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands
                Article
                TICS1216
                10.1016/j.tics.2013.06.007
                3730178
                23876494
                8e479e52-149a-4fde-afba-85adf2ec84d9
                © 2013 Elsevier Ltd.

                This document may be redistributed and reused, subject to certain conditions.

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                Neurosciences
                Neurosciences

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