Using drawings and deep neural networks to characterize the building blocks of human visual similarity.
Early in life and without special training, human beings discern resemblance between abstract visual stimuli, such as drawings, and the real-world objects they represent. We used this capacity for visual abstraction as a tool for evaluating deep neural networks (DNNs) as models of human visual perce...
| Publicado en: | Memory & Cognition Vol. 53; no. 1; pp. 219 - 242 |
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| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
Springer Nature
Jan2025
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=182537778&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 182537778 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 0090502X MEG jtl: Memory & Cognition issn: 0090502X maglogo: N pubinfo: dt: Jan2025 vid: 53 iid: 1 pid: 237 pub: Springer Nature artinfo: ui: 182537778 10.3758/s13421-024-01580-1 ppf: 219 ppct: 23 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1.3MB tig: atl: Using drawings and deep neural networks to characterize the building blocks of human visual similarity. aug: au: Mukherjee, Kushin Rogers, Timothy T. affil: https://ror.org/01y2jtd41 Department of Psychology & Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA su: Task performance Semantics Judgment (Psychology) Statistical models Drawing Statistical sampling Convolutional neural networks Descriptive statistics Deep learning Visual perception Factor analysis Regression analysis sug: subj: Task performance Semantics Judgment (Psychology) Marketing Research and Public Opinion Polling Statistical models Drawing Statistical sampling Convolutional neural networks Descriptive statistics Deep learning Visual perception Factor analysis Regression analysis keyword: Deep Learning Multi-modal models Perception Psychology and Cognitive Sciences Psychology Similarity Judgements Deep Learning Multi-modal models Perception Psychology and Cognitive Sciences Psychology Similarity Judgements ab: Early in life and without special training, human beings discern resemblance between abstract visual stimuli, such as drawings, and the real-world objects they represent. We used this capacity for visual abstraction as a tool for evaluating deep neural networks (DNNs) as models of human visual perception. Contrasting five contemporary DNNs, we evaluated how well each explains human similarity judgments among line drawings of recognizable and novel objects. For object sketches, human judgments were dominated by semantic category information; DNN representations contributed little additional information. In contrast, such features explained significant unique variance perceived similarity of abstract drawings. In both cases, a vision transformer trained to blend representations of images and their natural language descriptions showed the greatest ability to explain human perceptual similarity—an observation consistent with contemporary views of semantic representation and processing in the human mind and brain. Together, the results suggest that the building blocks of visual similarity may arise within systems that learn to use visual information, not for specific classification, but in service of generating semantic representations of objects. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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