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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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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. |
format | Online Article Text |
id | pubmed-10407052 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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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