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  1. Why psychologists should embrace rather than abandon DNNs.Galit Yovel & Naphtali Abudarham - 2023 - Behavioral and Brain Sciences 46:e414.
    Deep neural networks (DNNs) are powerful computational models, which generate complex, high-level representations that were missing in previous models of human cognition. By studying these high-level representations, psychologists can now gain new insights into the nature and origin of human high-level vision, which was not possible with traditional handcrafted models. Abandoning DNNs would be a huge oversight for psychological sciences.
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  • One-shot learning of view-invariant object representations in newborn chicks.Justin N. Wood & Samantha M. W. Wood - 2020 - Cognition 199 (C):104192.
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  • Spatial relation categorization in infants and deep neural networks.Guy Davidson, A. Emin Orhan & Brenden M. Lake - 2024 - Cognition 245 (C):105690.
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  • Learning the generative principles of a symbol system from limited examples.Lei Yuan, Violet Xiang, David Crandall & Linda Smith - 2020 - Cognition 200 (C):104243.
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  • Fixing the problems of deep neural networks will require better training data and learning algorithms.Drew Linsley & Thomas Serre - 2023 - Behavioral and Brain Sciences 46:e400.
    Bowers et al. argue that deep neural networks (DNNs) are poor models of biological vision because they often learn to rival human accuracy by relying on strategies that differ markedly from those of humans. We show that this problem is worsening as DNNs are becoming larger-scale and increasingly more accurate, and prescribe methods for building DNNs that can reliably model biological vision.
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  • The temporal structure of parent talk to toddlers about objects.Lauren K. Slone, Drew H. Abney, Linda B. Smith & Chen Yu - 2023 - Cognition 230 (C):105266.
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