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  1. Toward biologically plausible artificial vision.Mason Westfall - 2023 - Behavioral and Brain Sciences 46:e290.
    Quilty-Dunn et al. argue that deep convolutional neural networks (DCNNs) optimized for image classification exemplify structural disanalogies to human vision. A different kind of artificial vision – found in reinforcement-learning agents navigating artificial three-dimensional environments – can be expected to be more human-like. Recent work suggests that language-like representations substantially improves these agents’ performance, lending some indirect support to the language-of-thought hypothesis (LoTH).
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  • Toward an Atlas of Canonical Cognitive Mechanisms.Angelo Pirrone & Konstantinos Tsetsos - 2023 - Cognitive Science 47 (2):e13243.
    A central goal in Cognitive Science is understanding the mechanisms that underlie cognition. Here, we contend that Cognitive Science, despite intense multidisciplinary efforts, has furnished surprisingly few mechanistic insights. We attribute this slow mechanistic progress to the fact that cognitive scientists insist on performing underdetermined exercises, deriving overparametrized mechanistic theories of complex behaviors and seeking validation of these theories to the elusive notions of optimality and biological plausibility. We propose that mechanistic progress in Cognitive Science will accelerate once cognitive scientists (...)
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  • Divergences in color perception between deep neural networks and humans.Ethan O. Nadler, Elise Darragh-Ford, Bhargav Srinivasa Desikan, Christian Conaway, Mark Chu, Tasker Hull & Douglas Guilbeault - 2023 - Cognition 241 (C):105621.
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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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