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  1. Finding out about filling-in: A guide to perceptual completion for visual science and the philosophy of perception.Luiz Pessoa, Evan Thompson & Alva Noë - 1998 - Behavioral and Brain Sciences 21 (6):723-748.
    In visual science the term filling-inis used in different ways, which often leads to confusion. This target article presents a taxonomy of perceptual completion phenomena to organize and clarify theoretical and empirical discussion. Examples of boundary completion (illusory contours) and featural completion (color, brightness, motion, texture, and depth) are examined, and single-cell studies relevant to filling-in are reviewed and assessed. Filling-in issues must be understood in relation to theoretical issues about neuralignoring an absencejumping to a conclusionanalytic isomorphismCartesian materialism, a particular (...)
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  • How does a brain build a cognitive code?Stephen Grossberg - 1980 - Psychological Review 87 (1):1-51.
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  • The magical number 4 in short-term memory: A reconsideration of mental storage capacity.Nelson Cowan - 2001 - Behavioral and Brain Sciences 24 (1):87-114.
    Miller (1956) summarized evidence that people can remember about seven chunks in short-term memory (STM) tasks. However, that number was meant more as a rough estimate and a rhetorical device than as a real capacity limit. Others have since suggested that there is a more precise capacity limit, but that it is only three to five chunks. The present target article brings together a wide variety of data on capacity limits suggesting that the smaller capacity limit is real. Capacity limits (...)
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  • Connectionism and cognitive architecture: A critical analysis.Jerry A. Fodor & Zenon W. Pylyshyn - 1988 - Cognition 28 (1-2):3-71.
    This paper explores the difference between Connectionist proposals for cognitive a r c h i t e c t u r e a n d t h e s o r t s o f m o d e l s t hat have traditionally been assum e d i n c o g n i t i v e s c i e n c e . W e c l a i m t h a t t h (...)
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  • Evaluating (and Improving) the Correspondence Between Deep Neural Networks and Human Representations.Joshua C. Peterson, Joshua T. Abbott & Thomas L. Griffiths - 2018 - Cognitive Science 42 (8):2648-2669.
    Decades of psychological research have been aimed at modeling how people learn features and categories. The empirical validation of these theories is often based on artificial stimuli with simple representations. Recently, deep neural networks have reached or surpassed human accuracy on tasks such as identifying objects in natural images. These networks learn representations of real‐world stimuli that can potentially be leveraged to capture psychological representations. We find that state‐of‐the‐art object classification networks provide surprisingly accurate predictions of human similarity judgments for (...)
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  • Dynamic binding in a neural network for shape recognition.John E. Hummel & Irving Biederman - 1992 - Psychological Review 99 (3):480-517.
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  • Visual search and stimulus similar¬ity.John Duncan & Glyn W. Humphreys - 1989 - Psychological Review 96 (3):433-458.
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  • Neural dynamics of form perception: Boundary completion, illusory figures, and neon color spreading.Stephen Grossberg & Ennio Mingolla - 1985 - Psychological Review 92 (2):173-211.
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  • Robust representation of shape in a Grey parrot.Irene M. Pepperberg & Ken Nakayama - 2016 - Cognition 153 (C):146-160.
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  • On language and connectionism: Analysis of a parallel distributed processing model of language acquisition.Steven Pinker & Alan Prince - 1988 - Cognition 28 (1-2):73-193.
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  • Neural dynamics of grouping and segmentation explain properties of visual crowding.Gregory Francis, Mauro Manassi & Michael H. Herzog - 2017 - Psychological Review 124 (4):483-504.
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  • Recognition-by-components: A theory of human image understanding.Irving Biederman - 1987 - Psychological Review 94 (2):115-147.
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  • Change blindness.Daniel J. Simons & Daniel T. Levin - 1997 - Trends in Cognitive Sciences 1 (1):241-82.
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  • The proper treatment of symbols in a connectionist architecture.Keith J. Holyoak & John E. Hummel - 2000 - In Eric Dietrich Art Markman (ed.), Cognitive Dynamics: Conceptual change in humans and machines. Lawrence Erlbaum. pp. 229--263.
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  • Color, consciousness, and the isomorphism constraint.Stephen E. Palmer - 1999 - Behavioral and Brain Sciences 22 (6):923-943.
    The relations among consciousness, brain, behavior, and scientific explanation are explored in the domain of color perception. Current scientific knowledge about color similarity, color composition, dimensional structure, unique colors, and color categories is used to assess Locke.
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  • A theory of relation learning and cross-domain generalization.Leonidas A. A. Doumas, Guillermo Puebla, Andrea E. Martin & John E. Hummel - 2022 - Psychological Review 129 (5):999-1041.
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  • Visual shape perception as Bayesian inference of 3D object-centered shape representations.Goker Erdogan & Robert A. Jacobs - 2017 - Psychological Review 124 (6):740-761.
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  • Where view-based theories of human object recognition break down: the role of structure in human shape perception.J. E. Hummel - 2000 - In Eric Dietrich Art Markman (ed.), Cognitive Dynamics: Conceptual change in humans and machines. Lawrence Erlbaum. pp. 157--185.
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