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  1. Putting knowledge into a visual shape representation.Eric Saund - 1992 - Artificial Intelligence 54 (1-2):71-119.
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  • A cognitive architecture for artificial vision.A. Chella, M. Frixione & S. Gaglio - 1997 - Artificial Intelligence 89 (1-2):73-111.
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  • Local contour symmetry facilitates scene categorization.John Wilder, Morteza Rezanejad, Sven Dickinson, Kaleem Siddiqi, Allan Jepson & Dirk B. Walther - 2019 - Cognition 182 (C):307-317.
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  • Preattentive recovery of three-dimensional orientation from line drawings.James T. Enns & Ronald A. Rensink - 1991 - Psychological Review 98 (3):335-351.
    It has generally been assumed that rapid visual search is based on simple features and that spatial relations between features are irrelevant for this task. Seven experiments involving search for line drawings contradict this assumption; a major determinant of search is the presence of line junctions. Arrow- and Y-junctions were detected rapidly in isolation and when they were embedded in drawings of rectangular polyhedra. Search for T-junctions was considerably slower. Drawings containing T-junctions often gave rise to very slow search even (...)
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  • Connectionist value units: Some concerns.John A. Barnden - 1986 - Behavioral and Brain Sciences 9 (1):92-93.
    This paper is a commentary on the target article by Dana H. Ballard, “Cortical connections and parallel processing: Structure and function”, in the same issue of the journal, pp. 67–120. -/- I raise some issues about the connectionist or neural-network implementation of information and information processing. Issues include the sharing of information by different parts of a connectionist/neural network, the copying of complex information from one place to another in a network, the possibility of connection weights not being synaptic weights, (...)
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  • A process-grammar for shape.Michael Leyton - 1988 - Artificial Intelligence 34 (2):213-247.
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  • Cortical connections and parallel processing: Structure and function.Dana H. Ballard - 1986 - Behavioral and Brain Sciences 9 (1):67-90.
    The cerebral cortex is a rich and diverse structure that is the basis of intelligent behavior. One of the deepest mysteries of the function of cortex is that neural processing times are only about one hundred times as fast as the fastest response times for complex behavior. At the very least, this would seem to indicate that the cortex does massive amounts of parallel computation.This paper explores the hypothesis that an important part of the cortex can be modeled as a (...)
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  • What does the cortex do?Mriganka Sur - 1986 - Behavioral and Brain Sciences 9 (1):105-105.
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  • S eeingand visualizing: I T' S n otwhaty ou T hink.Zenon Pylyshyn - unknown
    6. Seeing With the Mind’s Eye 1: The Puzzle of Mental Imagery .................................................6-1 6.1 What is the puzzle about mental imagery?..............................................................................6-1 6.2 Content, form and substance of representations ......................................................................6-6 6.3 What is responsible for the pattern of results obtained in imagery studies?.................................6-8..
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  • The role of location indexes in spatial perception: A sketch of the FINST spatial-index model.Zenon Pylyshyn - 1989 - Cognition 32 (1):65-97.
    Marr (1982) may have been one of the rst vision researchers to insist that in modeling vision it is important to separate the location of visual features from their type. He argued that in early stages of visual processing there must be “place tokens” that enable subsequent stages of the visual system to treat locations independent of what specic feature type was at that location. Thus, in certain respects a collinear array of diverse features could still be perceived as a (...)
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  • What's in the term connectionist?.Christof Koch - 1986 - Behavioral and Brain Sciences 9 (1):100-101.
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  • Connectionist computing and neural machinery: Examining the test of “timing”.John K. Tsotsos - 1986 - Behavioral and Brain Sciences 9 (1):106-107.
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  • Computational neuroscience.Terrence J. Sejnowski - 1986 - Behavioral and Brain Sciences 9 (1):104-105.
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  • Part-Based Segmentation and Modeling of Range Data by Moving Target.Roberto Pirrone - 2001 - Journal of Intelligent Systems 11 (4):217-248.
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  • Old dogmas and new axioms in brain theory.Andràs J. Pellionisz - 1986 - Behavioral and Brain Sciences 9 (1):103-104.
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  • Salience of visual parts.Donald D. Hoffman & Manish Singh - 1997 - Cognition 63 (1):29-78.
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  • Two tests for the value unit model: Multicell recordings and pointers.David Mumford - 1986 - Behavioral and Brain Sciences 9 (1):102-103.
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  • The gap from sensation to cognition.Michael S. Landy - 1986 - Behavioral and Brain Sciences 9 (1):101-102.
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  • “Grandmother networks” and computational economy.J. J. Hopfield - 1986 - Behavioral and Brain Sciences 9 (1):100-100.
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  • Invariant and programmable neuropsychological systems are fibrations.William C. Hoffman - 1986 - Behavioral and Brain Sciences 9 (1):99-100.
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  • Does the brain compute?Erich Harth - 1986 - Behavioral and Brain Sciences 9 (1):98-99.
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  • Brain metaphors, theories, and facts.Stephen Grossberg - 1986 - Behavioral and Brain Sciences 9 (1):97-98.
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  • Mental Representation, Conceptual Spaces and Metaphors.Peter Gärdenfors - 1996 - Synthese 106 (1):21 - 47.
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  • Cortical architectures and value unit encoding.Charles D. Gilbert - 1986 - Behavioral and Brain Sciences 9 (1):96-97.
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  • Visual Knowledge Representation of Moving Scenes.A. Chella, Μ Frixione & S. Gaglio - 2000 - Journal of Intelligent Systems 10 (4):377-404.
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  • Integrating Subsymbolic and Symbolic Processing in Artificial Vision. E. Ardizzone, A. Chella, M. Frixione & S. Gaglio - 1992 - Journal of Intelligent Systems 1 (4):273-308.
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  • Abstract solutions versus neurobiologically plausible problems.Jeffrey Foss - 1986 - Behavioral and Brain Sciences 9 (1):95-96.
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  • What's the connection?Leif H. Finkel & George N. Reeke - 1986 - Behavioral and Brain Sciences 9 (1):94-95.
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  • Phase-space representation and coordinate transformation: A general paradigm for neural computation.Paul M. Churchland - 1986 - Behavioral and Brain Sciences 9 (1):93-94.
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  • Understanding dynamic scenes.A. Chella, M. Frixione & S. Gaglio - 2000 - Artificial Intelligence 123 (1-2):89-132.
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  • Value units make the right connections.Dana H. Ballard - 1986 - Behavioral and Brain Sciences 9 (1):107-120.
    The cerebral cortex is a rich and diverse structure that is the basis of intelligent behavior. One of the deepest mysteries of the function of cortex is that neural processing times are only about one hundred times as fast as the fastest response times for complex behavior. At the very least, this would seem to indicate that the cortex does massive amounts of parallel computation.This paper explores the hypothesis that an important part of the cortex can be modeled as a (...)
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  • Value encoding of patterns and variable encoding of transformations?John C. Baird - 1986 - Behavioral and Brain Sciences 9 (1):91-92.
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  • Value, variable, and coarse coding by posterior parietal neurons.Richard A. Andersen - 1986 - Behavioral and Brain Sciences 9 (1):90-91.
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