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Where view-based theories of human object recognition break down: the role of structure in human shape perception

In Eric Dietrich Art Markman (ed.), Cognitive Dynamics: Conceptual change in humans and machines. Lawrence Erlbaum. pp. 157--185 (2000)

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  1. The best game in town: The reemergence of the language-of-thought hypothesis across the cognitive sciences.Jake Quilty-Dunn, Nicolas Porot & Eric Mandelbaum - 2023 - Behavioral and Brain Sciences 46:e261.
    Mental representations remain the central posits of psychology after many decades of scrutiny. However, there is no consensus about the representational format(s) of biological cognition. This paper provides a survey of evidence from computational cognitive psychology, perceptual psychology, developmental psychology, comparative psychology, and social psychology, and concludes that one type of format that routinely crops up is the language-of-thought (LoT). We outline six core properties of LoTs: (i) discrete constituents; (ii) role-filler independence; (iii) predicate–argument structure; (iv) logical operators; (v) inferential (...)
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  • Towards structural systematicity in distributed, statically bound visual representations.Shimon Edelman & Nathan Intrator - 2003 - Cognitive Science 27 (1):73-109.
    The problem of representing the spatial structure of images, which arises in visual object processing, is commonly described using terminology borrowed from propositional theories of cognition, notably, the concept of compositionality. The classical propositional stance mandates representations composed of symbols, which stand for atomic or composite entities and enter into arbitrarily nested relationships. We argue that the main desiderata of a representational system—productivity and systematicity—can (indeed, for a number of reasons, should) be achieved without recourse to the classical, proposition‐like compositionality. (...)
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  • Deep problems with neural network models of human vision.Jeffrey S. Bowers, Gaurav Malhotra, Marin Dujmović, Milton Llera Montero, Christian Tsvetkov, Valerio Biscione, Guillermo Puebla, Federico Adolfi, John E. Hummel, Rachel F. Heaton, Benjamin D. Evans, Jeffrey Mitchell & Ryan Blything - 2023 - Behavioral and Brain Sciences 46:e385.
    Deep neural networks (DNNs) have had extraordinary successes in classifying photographic images of objects and are often described as the best models of biological vision. This conclusion is largely based on three sets of findings: (1) DNNs are more accurate than any other model in classifying images taken from various datasets, (2) DNNs do the best job in predicting the pattern of human errors in classifying objects taken from various behavioral datasets, and (3) DNNs do the best job in predicting (...)
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  • Better limited systematicity in hand than structural descriptions in the bush: A reply to Hummel.Shimon Edelman & Nathan Intrator - 2003 - Cognitive Science 27 (2):331-332.
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  • Visual Processing of Object Structure.Shimon Edelman & Nathan Intrator - 2002 - In Michael A. Arbib (ed.), The Handbook of Brain Theory and Neural Networks, Second Edition. MIT Press.
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  • “Effective systematicity” in, “effective systematicity” out: a reply to Edelman and Intrator.John E. Hummel - 2003 - Cognitive Science 27 (2):327-329.
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