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      Neural codes of seeing architectural styles

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          Abstract

          Images of iconic buildings, such as the CN Tower, instantly transport us to specific places, such as Toronto. Despite the substantial impact of architectural design on people’s visual experience of built environments, we know little about its neural representation in the human brain. In the present study, we have found patterns of neural activity associated with specific architectural styles in several high-level visual brain regions, but not in primary visual cortex (V1). This finding suggests that the neural correlates of the visual perception of architectural styles stem from style-specific complex visual structure beyond the simple features computed in V1. Surprisingly, the network of brain regions representing architectural styles included the fusiform face area (FFA) in addition to several scene-selective regions. Hierarchical clustering of error patterns further revealed that the FFA participated to a much larger extent in the neural encoding of architectural styles than entry-level scene categories. We conclude that the FFA is involved in fine-grained neural encoding of scenes at a subordinate-level, in our case, architectural styles of buildings. This study for the first time shows how the human visual system encodes visual aspects of architecture, one of the predominant and longest-lasting artefacts of human culture.

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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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            Acquiring linear subspaces for face recognition under variable lighting.

            Previous work has demonstrated that the image variation of many objects (human faces in particular) under variable lighting can be effectively modeled by low-dimensional linear spaces, even when there are multiple light sources and shadowing. Basis images spanning this space are usually obtained in one of three ways: A large set of images of the object under different lighting conditions is acquired, and principal component analysis (PCA) is used to estimate a subspace. Alternatively, synthetic images are rendered from a 3D model (perhaps reconstructed from images) under point sources and, again, PCA is used to estimate a subspace. Finally, images rendered from a 3D model under diffuse lighting based on spherical harmonics are directly used as basis images. In this paper, we show how to arrange physical lighting so that the acquired images of each object can be directly used as the basis vectors of a low-dimensional linear space and that this subspace is close to those acquired by the other methods. More specifically, there exist configurations of k point light source directions, with k typically ranging from 5 to 9, such that, by taking k images of an object under these single sources, the resulting subspace is an effective representation for recognition under a wide range of lighting conditions. Since the subspace is generated directly from real images, potentially complex and/or brittle intermediate steps such as 3D reconstruction can be completely avoided; nor is it necessary to acquire large numbers of training images or to physically construct complex diffuse (harmonic) light fields. We validate the use of subspaces constructed in this fashion within the context of face recognition.
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              Representation of perceived object shape by the human lateral occipital complex.

              The human lateral occipital complex (LOC) has been implicated in object recognition, but it is unknown whether this region represents low-level image features or perceived object shape. We used an event-related functional magnetic resonance imaging adaptation paradigm in which the response to pairs of successively presented stimuli is lower when they are identical than when they are different. Adaptation across a change between the two stimuli in a pair provides evidence for a common neural representation invariant to that change. We found adaptation in the LOC when perceived shape was identical but contours differed, but not when contours were identical but perceived shape differed. These data indicate that the LOC represents not simple image features, but rather higher level shape information.
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                Author and article information

                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group
                2045-2322
                10 January 2017
                2017
                : 7
                : 40201
                Affiliations
                [1 ]Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign , Urbana, Illinois, 61801, United States
                [2 ]Department of City and Regional Planning, The Ohio State University Columbus , Ohio, 43210, United States
                [3 ]Department of Psychology, University of Toronto , Toronto, Ontario, M5S 3G3, Canada
                Author notes
                Article
                srep40201
                10.1038/srep40201
                5223202
                28071765
                c6d40262-632f-4dd9-82e7-e8db312bebad
                Copyright © 2017, The Author(s)

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                : 29 April 2016
                : 05 December 2016
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