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Investigating the Impact of the Missing Significant Objects in Scene Recognition Using Multivariate Pattern Analysis

Significant objects in a scene can make a great contribution to scene recognition. Besides the three scene-selective regions: parahippocampal place area (PPA), retrosplenial complex (RSC), and occipital place area (OPA), some neuroimaging studies have shown that the lateral occipital complex (LOC) i...

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Autores principales: Gu, Jin, Liu, Baolin, Yan, Weiran, Miao, Qiaomu, Wei, Jianguo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7773817/
https://www.ncbi.nlm.nih.gov/pubmed/33390924
http://dx.doi.org/10.3389/fnbot.2020.597471
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author Gu, Jin
Liu, Baolin
Yan, Weiran
Miao, Qiaomu
Wei, Jianguo
author_facet Gu, Jin
Liu, Baolin
Yan, Weiran
Miao, Qiaomu
Wei, Jianguo
author_sort Gu, Jin
collection PubMed
description Significant objects in a scene can make a great contribution to scene recognition. Besides the three scene-selective regions: parahippocampal place area (PPA), retrosplenial complex (RSC), and occipital place area (OPA), some neuroimaging studies have shown that the lateral occipital complex (LOC) is also engaged in scene recognition processing. In this study, the multivariate pattern analysis was adopted to explore the object-scene association in scene recognition when different amounts of significant objects were masked. The scene classification only succeeded in the intact scene in the ROIs. In addition, the average signal intensity in LOC [including the lateral occipital cortex (LO) and the posterior fusiform area (pF)] decreased when there were masked objects, but such a decrease was not observed in scene-selective regions. These results suggested that LOC was sensitive to the loss of significant objects and mainly involved in scene recognition by the object-scene semantic association. The performance of the scene-selective areas may be mainly due to the fact that they responded to the change of the scene's entire attribute, such as the spatial information, when they were employed in the scene recognition processing. These findings further enrich our knowledge of the significant objects' influence on the activation pattern during the process of scene recognition.
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spelling pubmed-77738172021-01-01 Investigating the Impact of the Missing Significant Objects in Scene Recognition Using Multivariate Pattern Analysis Gu, Jin Liu, Baolin Yan, Weiran Miao, Qiaomu Wei, Jianguo Front Neurorobot Neuroscience Significant objects in a scene can make a great contribution to scene recognition. Besides the three scene-selective regions: parahippocampal place area (PPA), retrosplenial complex (RSC), and occipital place area (OPA), some neuroimaging studies have shown that the lateral occipital complex (LOC) is also engaged in scene recognition processing. In this study, the multivariate pattern analysis was adopted to explore the object-scene association in scene recognition when different amounts of significant objects were masked. The scene classification only succeeded in the intact scene in the ROIs. In addition, the average signal intensity in LOC [including the lateral occipital cortex (LO) and the posterior fusiform area (pF)] decreased when there were masked objects, but such a decrease was not observed in scene-selective regions. These results suggested that LOC was sensitive to the loss of significant objects and mainly involved in scene recognition by the object-scene semantic association. The performance of the scene-selective areas may be mainly due to the fact that they responded to the change of the scene's entire attribute, such as the spatial information, when they were employed in the scene recognition processing. These findings further enrich our knowledge of the significant objects' influence on the activation pattern during the process of scene recognition. Frontiers Media S.A. 2020-12-17 /pmc/articles/PMC7773817/ /pubmed/33390924 http://dx.doi.org/10.3389/fnbot.2020.597471 Text en Copyright © 2020 Gu, Liu, Yan, Miao and Wei. http://creativecommons.org/licenses/by/4.0/ 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(s) 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.
spellingShingle Neuroscience
Gu, Jin
Liu, Baolin
Yan, Weiran
Miao, Qiaomu
Wei, Jianguo
Investigating the Impact of the Missing Significant Objects in Scene Recognition Using Multivariate Pattern Analysis
title Investigating the Impact of the Missing Significant Objects in Scene Recognition Using Multivariate Pattern Analysis
title_full Investigating the Impact of the Missing Significant Objects in Scene Recognition Using Multivariate Pattern Analysis
title_fullStr Investigating the Impact of the Missing Significant Objects in Scene Recognition Using Multivariate Pattern Analysis
title_full_unstemmed Investigating the Impact of the Missing Significant Objects in Scene Recognition Using Multivariate Pattern Analysis
title_short Investigating the Impact of the Missing Significant Objects in Scene Recognition Using Multivariate Pattern Analysis
title_sort investigating the impact of the missing significant objects in scene recognition using multivariate pattern analysis
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7773817/
https://www.ncbi.nlm.nih.gov/pubmed/33390924
http://dx.doi.org/10.3389/fnbot.2020.597471
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