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Semantic Relatedness Emerges in Deep Convolutional Neural Networks Designed for Object Recognition
Human not only can effortlessly recognize objects, but also characterize object categories into semantic concepts with a nested hierarchical structure. One dominant view is that top-down conceptual guidance is necessary to form such hierarchy. Here we challenged this idea by examining whether deep c...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7938322/ https://www.ncbi.nlm.nih.gov/pubmed/33692678 http://dx.doi.org/10.3389/fncom.2021.625804 |
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author | Huang, Taicheng Zhen, Zonglei Liu, Jia |
author_facet | Huang, Taicheng Zhen, Zonglei Liu, Jia |
author_sort | Huang, Taicheng |
collection | PubMed |
description | Human not only can effortlessly recognize objects, but also characterize object categories into semantic concepts with a nested hierarchical structure. One dominant view is that top-down conceptual guidance is necessary to form such hierarchy. Here we challenged this idea by examining whether deep convolutional neural networks (DCNNs) could learn relations among objects purely based on bottom-up perceptual experience of objects through training for object categorization. Specifically, we explored representational similarity among objects in a typical DCNN (e.g., AlexNet), and found that representations of object categories were organized in a hierarchical fashion, suggesting that the relatedness among objects emerged automatically when learning to recognize them. Critically, the emerged relatedness of objects in the DCNN was highly similar to the WordNet in human, implying that top-down conceptual guidance may not be a prerequisite for human learning the relatedness among objects. In addition, the developmental trajectory of the relatedness among objects during training revealed that the hierarchical structure was constructed in a coarse-to-fine fashion, and evolved into maturity before the establishment of object recognition ability. Finally, the fineness of the relatedness was greatly shaped by the demand of tasks that the DCNN performed, as the higher superordinate level of object classification was, the coarser the hierarchical structure of the relatedness emerged. Taken together, our study provides the first empirical evidence that semantic relatedness of objects emerged as a by-product of object recognition in DCNNs, implying that human may acquire semantic knowledge on objects without explicit top-down conceptual guidance. |
format | Online Article Text |
id | pubmed-7938322 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-79383222021-03-09 Semantic Relatedness Emerges in Deep Convolutional Neural Networks Designed for Object Recognition Huang, Taicheng Zhen, Zonglei Liu, Jia Front Comput Neurosci Neuroscience Human not only can effortlessly recognize objects, but also characterize object categories into semantic concepts with a nested hierarchical structure. One dominant view is that top-down conceptual guidance is necessary to form such hierarchy. Here we challenged this idea by examining whether deep convolutional neural networks (DCNNs) could learn relations among objects purely based on bottom-up perceptual experience of objects through training for object categorization. Specifically, we explored representational similarity among objects in a typical DCNN (e.g., AlexNet), and found that representations of object categories were organized in a hierarchical fashion, suggesting that the relatedness among objects emerged automatically when learning to recognize them. Critically, the emerged relatedness of objects in the DCNN was highly similar to the WordNet in human, implying that top-down conceptual guidance may not be a prerequisite for human learning the relatedness among objects. In addition, the developmental trajectory of the relatedness among objects during training revealed that the hierarchical structure was constructed in a coarse-to-fine fashion, and evolved into maturity before the establishment of object recognition ability. Finally, the fineness of the relatedness was greatly shaped by the demand of tasks that the DCNN performed, as the higher superordinate level of object classification was, the coarser the hierarchical structure of the relatedness emerged. Taken together, our study provides the first empirical evidence that semantic relatedness of objects emerged as a by-product of object recognition in DCNNs, implying that human may acquire semantic knowledge on objects without explicit top-down conceptual guidance. Frontiers Media S.A. 2021-02-22 /pmc/articles/PMC7938322/ /pubmed/33692678 http://dx.doi.org/10.3389/fncom.2021.625804 Text en Copyright © 2021 Huang, Zhen and Liu. 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 Huang, Taicheng Zhen, Zonglei Liu, Jia Semantic Relatedness Emerges in Deep Convolutional Neural Networks Designed for Object Recognition |
title | Semantic Relatedness Emerges in Deep Convolutional Neural Networks Designed for Object Recognition |
title_full | Semantic Relatedness Emerges in Deep Convolutional Neural Networks Designed for Object Recognition |
title_fullStr | Semantic Relatedness Emerges in Deep Convolutional Neural Networks Designed for Object Recognition |
title_full_unstemmed | Semantic Relatedness Emerges in Deep Convolutional Neural Networks Designed for Object Recognition |
title_short | Semantic Relatedness Emerges in Deep Convolutional Neural Networks Designed for Object Recognition |
title_sort | semantic relatedness emerges in deep convolutional neural networks designed for object recognition |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7938322/ https://www.ncbi.nlm.nih.gov/pubmed/33692678 http://dx.doi.org/10.3389/fncom.2021.625804 |
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