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Visual number sense for real-world scenes shared by deep neural networks and humans

Recently, visual number sense has been identified from deep neural networks (DNNs). However, whether DNNs have the same capacity for real-world scenes, rather than the simple geometric figures that are often tested, is unclear. In this study, we explore the number perception of scenes using AlexNet...

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Autores principales: Wencheng, Wu, Ge, Yingxi, Zuo, Zhentao, Chen, Lin, Qin, Xu, Zuxiang, Liu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10407052/
https://www.ncbi.nlm.nih.gov/pubmed/37560656
http://dx.doi.org/10.1016/j.heliyon.2023.e18517
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author Wencheng, Wu
Ge, Yingxi
Zuo, Zhentao
Chen, Lin
Qin, Xu
Zuxiang, Liu
author_facet Wencheng, Wu
Ge, Yingxi
Zuo, Zhentao
Chen, Lin
Qin, Xu
Zuxiang, Liu
author_sort Wencheng, Wu
collection PubMed
description Recently, visual number sense has been identified from deep neural networks (DNNs). However, whether DNNs have the same capacity for real-world scenes, rather than the simple geometric figures that are often tested, is unclear. In this study, we explore the number perception of scenes using AlexNet and find that numerosity can be represented by the pattern of group activation of the category layer units. The global activation of these units increases with the number of objects in the scene, and the variations in their activation decrease accordingly. By decoding the numerosity from this pattern, we reveal that the embedding coefficient of a scene determines the likelihood of potential objects to contribute to numerical perception. This was demonstrated by the more optimized performance for pictures with relatively high embedding coefficients in both DNNs and humans. This study for the first time shows that a distinct feature in visual environments, revealed by DNNs, can modulate human perception, supported by a group-coding mechanism.
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spelling pubmed-104070522023-08-09 Visual number sense for real-world scenes shared by deep neural networks and humans Wencheng, Wu Ge, Yingxi Zuo, Zhentao Chen, Lin Qin, Xu Zuxiang, Liu Heliyon Research Article Recently, visual number sense has been identified from deep neural networks (DNNs). However, whether DNNs have the same capacity for real-world scenes, rather than the simple geometric figures that are often tested, is unclear. In this study, we explore the number perception of scenes using AlexNet and find that numerosity can be represented by the pattern of group activation of the category layer units. The global activation of these units increases with the number of objects in the scene, and the variations in their activation decrease accordingly. By decoding the numerosity from this pattern, we reveal that the embedding coefficient of a scene determines the likelihood of potential objects to contribute to numerical perception. This was demonstrated by the more optimized performance for pictures with relatively high embedding coefficients in both DNNs and humans. This study for the first time shows that a distinct feature in visual environments, revealed by DNNs, can modulate human perception, supported by a group-coding mechanism. Elsevier 2023-07-24 /pmc/articles/PMC10407052/ /pubmed/37560656 http://dx.doi.org/10.1016/j.heliyon.2023.e18517 Text en © 2023 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Research Article
Wencheng, Wu
Ge, Yingxi
Zuo, Zhentao
Chen, Lin
Qin, Xu
Zuxiang, Liu
Visual number sense for real-world scenes shared by deep neural networks and humans
title Visual number sense for real-world scenes shared by deep neural networks and humans
title_full Visual number sense for real-world scenes shared by deep neural networks and humans
title_fullStr Visual number sense for real-world scenes shared by deep neural networks and humans
title_full_unstemmed Visual number sense for real-world scenes shared by deep neural networks and humans
title_short Visual number sense for real-world scenes shared by deep neural networks and humans
title_sort visual number sense for real-world scenes shared by deep neural networks and humans
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10407052/
https://www.ncbi.nlm.nih.gov/pubmed/37560656
http://dx.doi.org/10.1016/j.heliyon.2023.e18517
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