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• W3174811924 abstract "Abstract The investigation of visual categorization has recently been aided by the introduction of deep convolutional neural networks (CNNs), which achieve unprecedented accuracy in picture classification after extensive training. Even if the architecture of CNNs is inspired by the organization of the visual brain, the similarity between CNN and human visual processing remains unclear. Here, we investigated this issue by engaging humans and CNNs in a two‐class visual categorization task. To this end, pictures containing animals or vehicles were modified to contain only low/high spatial frequency (HSF) information, or were scrambled in the phase of the spatial frequency spectrum. For all types of degradation, accuracy increased as degradation was reduced for both humans and CNNs; however, the thresholds for accurate categorization varied between humans and CNNs. More remarkable differences were observed for HSF information compared to the other two types of degradation, both in terms of overall accuracy and image‐level agreement between humans and CNNs. The difficulty with which the CNNs were shown to categorize high‐passed natural scenes was reduced by picture whitening, a procedure which is inspired by how visual systems process natural images. The results are discussed concerning the adaptation to regularities in the visual environment (scene statistics); if the visual characteristics of the environment are not learned by CNNs, their visual categorization may depend only on a subset of the visual information on which humans rely, for example, on low spatial frequency information." @default.
• W3174811924 created "2021-07-05" @default.
• W3174811924 creator A5030507840 @default.
• W3174811924 creator A5032626782 @default.
• W3174811924 creator A5048080199 @default.
• W3174811924 creator A5062424045 @default.
• W3174811924 date "2021-06-01" @default.
• W3174811924 modified "2023-10-16" @default.
• W3174811924 title "Do Humans and Deep Convolutional Neural Networks Use Visual Information Similarly for the Categorization of Natural Scenes?" @default.
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• W3174811924 doi "https://doi.org/10.1111/cogs.13009" @default.
• W3174811924 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8365760" @default.
• W3174811924 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34170027" @default.
• W3174811924 hasPublicationYear "2021" @default.
• W3174811924 type Work @default.

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