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  1. Neural Computation of Surface Border Ownership and Relative Surface Depth from Ambiguous Contrast Inputs.Birgitta Dresp-Langley & Stephen Grossberg - 2016 - Frontiers in Psychology 7.
    The segregation of image parts into foreground and background is an important aspect of the neural computation of 3D scene perception. To achieve such segregation, the brain needs information about border ownership; that is, the belongingness of a contour to a specific surface represented in the image. This article presents psychophysical data derived from 3D percepts of figure and ground that were generated by presenting 2D images composed of spatially disjoint shapes that pointed inward or outward relative to the continuous (...)
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  • Four theories of amodal perception.Bence Nanay - 2007 - Proceedings of the 29th Annual Conference of the Cognitive Science Society.
    We are aware of those parts of a cat that are occluded behind a fence. The question is how we represent these occluded parts of perceived objects: this is the problem of amodal perception. I will consider four theories and compare their explanatory power: (i) we see them, (ii) we have nonperceptual beliefs about them, (iii) we have immediate perceptual access to them and (iv) we visualize them. I point out that the first three of these views face both empirical (...)
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  • Neurogeometry of Perception: Isotropic and Anisotropic Aspects.Giovanna Citti & Alessandro Sarti - 2022 - Axiomathes 32 (5):817-840.
    In this paper we first recall geometrical models of neurogeometical in Lie groups and we show that geometrical properties of horizontal cortical connectivity can be considered as a neural correlate of a geometry of the visual plane. Then we introduce a new model of non isotropic cortical connectivity modeled on statistics of images. In this way we are able to justify oblique phenomena comparable with experimental findings.
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  • The quest for plausibility: A negative heuristic for science?R. W. Byrne - 1991 - Behavioral and Brain Sciences 14 (2):217-218.
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  • Object completion effects in attention and memory.Siyi Chen - 2018 - Dissertation, Ludwig-Maximilians-Universität
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  • A Unified Cognitive Model of Visual Filling-In Based on an Emergic Network Architecture.David Pierre Leibovitz - 2013 - Dissertation, Carleton University
    The Emergic Cognitive Model (ECM) is a unified computational model of visual filling-in based on the Emergic Network architecture. The Emergic Network was designed to help realize systems undergoing continuous change. In this thesis, eight different filling-in phenomena are demonstrated under a regime of continuous eye movement (and under static eye conditions as well). -/- ECM indirectly demonstrates the power of unification inherent with Emergic Networks when cognition is decomposed according to finer-grained functions supporting change. These can interact to raise (...)
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  • Neural Facades: Visual Representations of Static and Moving Form‐And‐Color‐And‐Depth.Stephen Grossberg - 1990 - Mind and Language 5 (4):411-456.
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  • Non-rigid illusory contours and global shape transformations defined by spatiotemporal boundary formation.Gennady Erlikhman, Yang Z. Xing & Philip J. Kellman - 2014 - Frontiers in Human Neuroscience 8.
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  • A cortical edge-integration model of object-based lightness computation that explains effects of spatial context and individual differences.Michael E. Rudd - 2014 - Frontiers in Human Neuroscience 8.
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  • Asymmetrical color filling-in from the nasal to the temporal side of the blind spot.Hui Li, Junxiang Luo, Yiliang Lu, Janis Kan, Lothar Spillmann & Wei Wang - 2014 - Frontiers in Human Neuroscience 8.
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  • Cortical dynamics of contextually cued attentive visual learning and search: Spatial and object evidence accumulation.Tsung-Ren Huang & Stephen Grossberg - 2010 - Psychological Review 117 (4):1080-1112.
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  • A contour propagation approach to surface filling-in and volume formation.Peter Ulric Tse - 2002 - Psychological Review 109 (1):91-115.
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  • Object Interpolation in Three Dimensions.Philip J. Kellman, Patrick Garrigan & Thomas F. Shipley - 2005 - Psychological Review 112 (3):586-609.
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  • Illusory Streaks from Corners and Their Perceptual Integration.Sergio Roncato, Stefano Guidi, Oronzo Parlangeli & Luca Battaglini - 2016 - Frontiers in Psychology 7.
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  • Flexible color perception depending on the shape and positioning of achromatic contours.Mark Vergeer, Stuart Anstis & Rob van Lier - 2015 - Frontiers in Psychology 6.
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  • How the venetian blind percept emerges from the laminar cortical dynamics of 3D vision.Yongqiang Cao & Stephen Grossberg - 2014 - Frontiers in Psychology 5.
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  • Is economics still immersed in the old concepts of the Enlightenment era?Andrzej P. Wierzbicki - 1991 - Behavioral and Brain Sciences 14 (2):236-237.
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  • The human being as a bumbling optimalist: A psychologist's viewpoint.Masanao Toda - 1991 - Behavioral and Brain Sciences 14 (2):235-235.
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  • Should the quest for optimality worry us?Nils-Eric Sahlin - 1991 - Behavioral and Brain Sciences 14 (2):231-231.
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  • Don't just sit there, optimise something.J. H. P. Paelinck - 1991 - Behavioral and Brain Sciences 14 (2):230-230.
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  • Complexity and optimality.Dauglas A. Miller & Steven W. Zucker - 1991 - Behavioral and Brain Sciences 14 (2):227-228.
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  • Two dynamic criteria for validating claims of optimality.Geoffrey F. Miller - 1991 - Behavioral and Brain Sciences 14 (2):228-229.
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  • The example of psychology: Optimism, not optimality.Daniel S. Levine - 1991 - Behavioral and Brain Sciences 14 (2):225-226.
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  • Straining the word “optimal”.James E. Mazur - 1991 - Behavioral and Brain Sciences 14 (2):227-227.
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  • Natural science, social science and optimality.Oleg Larichev - 1991 - Behavioral and Brain Sciences 14 (2):224-225.
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  • Why optimality is not worth arguing about.Stephen E. G. Lea - 1991 - Behavioral and Brain Sciences 14 (2):225-225.
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  • Optimality and constraint.David A. Helweg & Herbert L. Roitblat - 1991 - Behavioral and Brain Sciences 14 (2):222-223.
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  • Types of optimality: Who is the steersman?Michael E. Hyland - 1991 - Behavioral and Brain Sciences 14 (2):223-224.
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  • Natural selection doesn't have goals, but it's the reason organisms do.Martin Daly - 1991 - Behavioral and Brain Sciences 14 (2):219-220.
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  • Organisms, scientists and optimality.Michael Davison - 1991 - Behavioral and Brain Sciences 14 (2):220-221.
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  • Criteria for optimality.Michel Cabanac - 1991 - Behavioral and Brain Sciences 14 (2):218-218.
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  • Some optimality principles in evolution.James F. Crow - 1991 - Behavioral and Brain Sciences 14 (2):218-219.
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  • Optimality as a mathematical rhetoric for zeroes.Fred L. Bookstein - 1991 - Behavioral and Brain Sciences 14 (2):216-217.
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  • Optimality as an evaluative standard in the study of decision-making.Jonathan Baron - 1991 - Behavioral and Brain Sciences 14 (2):216-216.
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  • The quest for optimality: A positive heuristic of science?Paul J. H. Schoemaker - 1991 - Behavioral and Brain Sciences 14 (2):205-215.
    This paper examines the strengths and weaknesses of one of science's most pervasive and flexible metaprinciples;optimalityis used to explain utility maximization in economics, least effort principles in physics, entropy in chemistry, and survival of the fittest in biology. Fermat's principle of least time involves both teleological and causal considerations, two distinct modes of explanation resting on poorly understood psychological primitives. The rationality heuristic in economics provides an example from social science of the potential biases arising from the extreme flexibility of (...)
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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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  • Computational neuroscience.Terrence J. Sejnowski - 1986 - Behavioral and Brain Sciences 9 (1):104-105.
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  • What does the cortex do?Mriganka Sur - 1986 - Behavioral and Brain Sciences 9 (1):105-105.
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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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  • Cortical architectures and value unit encoding.Charles D. Gilbert - 1986 - Behavioral and Brain Sciences 9 (1):96-97.
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  • Brain metaphors, theories, and facts.Stephen Grossberg - 1986 - Behavioral and Brain Sciences 9 (1):97-98.
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  • Does the brain compute?Erich Harth - 1986 - Behavioral and Brain Sciences 9 (1):98-99.
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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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  • “Grandmother networks” and computational economy.J. J. Hopfield - 1986 - Behavioral and Brain Sciences 9 (1):100-100.
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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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  • The gap from sensation to cognition.Michael S. Landy - 1986 - Behavioral and Brain Sciences 9 (1):101-102.
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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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  • Old dogmas and new axioms in brain theory.Andràs J. Pellionisz - 1986 - Behavioral and Brain Sciences 9 (1):103-104.
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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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  • What's the connection?Leif H. Finkel & George N. Reeke - 1986 - Behavioral and Brain Sciences 9 (1):94-95.
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