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  1. Marr's Levels Revisited: Understanding How Brains Break.Valerie G. Hardcastle & Kiah Hardcastle - 2015 - Topics in Cognitive Science 7 (2):259-273.
    While the research programs in early cognitive science and artificial intelligence aimed to articulate what cognition was in ideal terms, much research in contemporary computational neuroscience looks at how and why brains fail to function as they should ideally. This focus on impairment affects how we understand David Marr's hypothesized three levels of understanding. In this essay, we suggest some refinements to Marr's distinctions using a population activity model of cortico-striatal circuitry exploring impulsivity and behavioral inhibition as a case study. (...)
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  • Sculpting the space of actions. Explaining human action by integrating intentions and mechanisms.Machiel Keestra - 2014 - Dissertation, University of Amsterdam
    How can we explain the intentional nature of an expert’s actions, performed without immediate and conscious control, relying instead on automatic cognitive processes? How can we account for the differences and similarities with a novice’s performance of the same actions? Can a naturalist explanation of intentional expert action be in line with a philosophical concept of intentional action? Answering these and related questions in a positive sense, this dissertation develops a three-step argument. Part I considers different methods of explanations in (...)
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  • Data and interpretation in comparative color vision.Gerald H. Jacobs - 1992 - Behavioral and Brain Sciences 15 (1):40-41.
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  • On possible perceptual worlds and how they shape their environments.Rainer J. Mausfeld, Reinhard M. Niederée & K. Dieter Heyer - 1992 - Behavioral and Brain Sciences 15 (1):47-48.
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  • Conclusions from color vision of insects.Werner Backhaus & Randolf Menzel - 1992 - Behavioral and Brain Sciences 15 (1):28-30.
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  • Why we view the brain as a computer.Oron Shagrir - 2006 - Synthese 153 (3):393-416.
    The view that the brain is a sort of computer has functioned as a theoretical guideline both in cognitive science and, more recently, in neuroscience. But since we can view every physical system as a computer, it has been less than clear what this view amounts to. By considering in some detail a seminal study in computational neuroscience, I first suggest that neuroscientists invoke the computational outlook to explain regularities that are formulated in terms of the information content of electrical (...)
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  • Ways of coloring.Evan Thompson, A. Palacios & F. J. Varela - 1992 - Behavioral and Brain Sciences 15 (1):1-26.
    Different explanations of color vision favor different philosophical positions: Computational vision is more compatible with objectivism (the color is in the object), psychophysics and neurophysiology with subjectivism (the color is in the head). Comparative research suggests that an explanation of color must be both experientialist (unlike objectivism) and ecological (unlike subjectivism). Computational vision's emphasis on optimally prespecified features of the environment (i.e., distal properties, independent of the sensory-motor capacities of the animal) is unsatisfactory. Conceiving of visual perception instead as the (...)
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  • Chomsky and Egan on computational theories of vision.Arnold Silverberg - 2006 - Minds and Machines 16 (4):495-524.
    Noam Chomsky and Frances Egan argue that David Marr.
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  • Individualism, computation, and perceptual content.Frances Egan - 1992 - Mind 101 (403):443-59.
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  • Supervenience and computational explanation in vision theory.Peter Morton - 1993 - Philosophy of Science 60 (1):86-99.
    According to Marr's theory of vision, computational processes of early vision rely for their success on certain "natural constraints" in the physical environment. I examine the implications of this feature of Marr's theory for the question whether psychological states supervene on neural states. It is reasonable to hold that Marr's theory is nonindividualistic in that, given the role of natural constraints, distinct computational theories of the same neural processes may be justified in different environments. But to avoid trivializing computational explanations, (...)
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  • Realism and instrumentalism in Bayesian cognitive science.Danielle Williams & Zoe Drayson - 2024 - In Tony Cheng, Ryoji Sato & Jakob Hohwy (eds.), Expected Experiences: The Predictive Mind in an Uncertain World. Routledge.
    There are two distinct approaches to Bayesian modelling in cognitive science. Black-box approaches use Bayesian theory to model the relationship between the inputs and outputs of a cognitive system without reference to the mediating causal processes; while mechanistic approaches make claims about the neural mechanisms which generate the outputs from the inputs. This paper concerns the relationship between these two approaches. We argue that the dominant trend in the philosophical literature, which characterizes the relationship between black-box and mechanistic approaches to (...)
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  • Ways of coloring: Comparative color vision as a case study for cognitive science.Evan Thompson, Adrian Palacios & Francisco J. Varela - 1992 - Behavioral and Brain Sciences 15 (1):1-26.
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  • A limited objectivism defended.Edward Wilson Averill - 1992 - Behavioral and Brain Sciences 15 (1):27-28.
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  • Levels of Explanation Vindicated.Víctor M. Verdejo & Daniel Quesada - 2011 - Review of Philosophy and Psychology 2 (1):77-88.
    Marr’s celebrated contribution to cognitive science (Marr 1982, chap. 1) was the introduction of (at least) three levels of description/explanation. However, most contemporary research has relegated the distinction between levels to a rather dispensable remark. Ignoring such an important contribution comes at a price, or so we shall argue. In the present paper, first we review Marr’s main points and motivations regarding levels of explanation. Second, we examine two cases in which the distinction between levels has been neglected when considering (...)
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  • Individualism and vision theory.Frances Egan - 1994 - Analysis 54 (4):258-264.
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  • Multivariant color vision.Peter Gouras - 1992 - Behavioral and Brain Sciences 15 (1):37-37.
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  • Color enactivism: A return to Kant?Paul R. Kinnear - 1992 - Behavioral and Brain Sciences 15 (1):41-41.
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  • The ethnocentricity of colour.J. van Brakel - 1992 - Behavioral and Brain Sciences 15 (1):53-54.
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  • Ways of coloring the ecological approach.Johan Wagemans & Charles M. M. de Weert - 1992 - Behavioral and Brain Sciences 15 (1):54-56.
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  • Hitting the nail on the head.Daniel C. Dennett - 1992 - Behavioral and Brain Sciences 15 (1):35-35.
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  • Grades of explanation in cognitive science.Richard Montgomery - 1998 - Synthese 114 (3):463-495.
    I sketch an explanatory framework that fits a variety of contemporary research programs in cognitive science. I then investigate the scope and the implications of this framework. The framework emphasizes (a) the explanatory role played by the semantic content of cognitive representations, and (b) the important mechanistic, non-intentional dimension of cognitive explanations. I show how both of these features are present simultaneously in certain varieties of cognitive explanation. I also consider the explanatory role played by grounded representational content, that is, (...)
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  • Computation, individuation, and the received view on representation.Mark Sprevak - 2010 - Studies in History and Philosophy of Science Part A 41 (3):260-270.
    The ‘received view’ about computation is that all computations must involve representational content. Egan and Piccinini argue against the received view. In this paper, I focus on Egan’s arguments, claiming that they fall short of establishing that computations do not involve representational content. I provide positive arguments explaining why computation has to involve representational content, and how that representational content may be of any type. I also argue that there is no need for computational psychology to be individualistic. Finally, I (...)
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  • In search of common features of animals' color vision systems and the constraints of environment.Erhard Maier & Dietrich Burkhardt - 1992 - Behavioral and Brain Sciences 15 (1):44-45.
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  • Color vision: Content versus experience.Mohan Matthen - 1992 - Behavioral and Brain Sciences 15 (1):46-47.
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  • Colors really are only in the head.James A. McGilvray - 1992 - Behavioral and Brain Sciences 15 (1):48-49.
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  • Wavelength processing and colour experience.Petra Stoerig & Alan Cowey - 1992 - Behavioral and Brain Sciences 15 (1):53-53.
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  • 1. Marr on Computational-Level Theories Marr on Computational-Level Theories (pp. 477-500).Oron Shagrir, John D. Norton, Holger Andreas, Jouni-Matti Kuukkanen, Aris Spanos, Eckhart Arnold, Elliott Sober, Peter Gildenhuys & Adela Helena Roszkowski - 2010 - Philosophy of Science 77 (4):477-500.
    According to Marr, a computational-level theory consists of two elements, the what and the why. This article highlights the distinct role of the Why element in the computational analysis of vision. Three theses are advanced: that the Why element plays an explanatory role in computational-level theories, that its goal is to explain why the computed function is appropriate for a given visual task, and that the explanation consists in showing that the functional relations between the representing cells are similar to (...)
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  • Marr’s Theory of Vision and the Argument From Success.Peter A. Morton - 1988 - PSA Proceedings of the Biennial Meeting of the Philosophy of Science Association 1988 (1):154-161.
    A central aspect of the computational theory of vision developed by Marr and his coworkers is the use made of contingent regularities in the physical environment to explain how the visual system determines the shape and location of objects in the world on the basis of the spatial organization of the retinal image. Marr (1982) refers to these environmental regularities as “natural constraints” and “physical assumptions.” In this paper I am concerned with recent arguments concerning the implications of this feature (...)
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  • On the ways to color.Evan Thompson, Adrian Palacios & Francisco J. Varela - 1992 - Behavioral and Brain Sciences 15 (1):56-74.
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  • Confusing structure and function.Kenneth M. Steele - 1992 - Behavioral and Brain Sciences 15 (1):52-53.
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  • Ecological subjectivism?Christine A. Skarda - 1992 - Behavioral and Brain Sciences 15 (1):51-52.
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  • What in the world determines the structure of color space?Roger N. Shepard - 1992 - Behavioral and Brain Sciences 15 (1):50-51.
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  • The content of Marr’s information-processing framework.J. Brendan Ritchie - 2019 - Philosophical Psychology 32 (7):1078-1099.
    ABSTRACTThe seminal work of David Marr, popularized in his classic work Vision, continues to exert a major influence on both cognitive science and philosophy. The interpretation of his work also co...
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  • Areas of ignorance and confusion in color science.Adam Reeves - 1992 - Behavioral and Brain Sciences 15 (1):49-50.
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  • Le Physique, le Morphologique, le Symbolique.Jean Petitot - 1990 - Revue de Synthèse 111 (1-2):139-183.
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  • Content, Computation and Externalism.Christopher Peacocke - 1995 - Philosophical Issues 6:227-264.
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  • On perceived colors.Christa Neumeyer - 1992 - Behavioral and Brain Sciences 15 (1):49-49.
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  • A mathematical framework for biological color vision.Laurence T. Maloney - 1992 - Behavioral and Brain Sciences 15 (1):45-46.
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  • Ontogeny and ontology: Ontophyletics and enactive focal vision.Barry Lia - 1992 - Behavioral and Brain Sciences 15 (1):43-44.
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  • Objectivism-subjectivim: A false dilemma?Joseph Levine - 1992 - Behavioral and Brain Sciences 15 (1):42-43.
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  • Ethological and ecological aspects of color vision.Sergei L. Kondrashev - 1992 - Behavioral and Brain Sciences 15 (1):42-42.
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  • The view of a computational animal.Anya Hurlbert - 1992 - Behavioral and Brain Sciences 15 (1):39-40.
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  • Comparative color vision and the objectivity of color.David Hilbert - 1992 - Behavioral and Brain Sciences 15 (1):38-39.
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  • Distinctions without differences: Commentary on Horgan and Tienson's connectionism and the philosophy of psychology.Valerie Gray Hardcastle - 1997 - Philosophical Psychology 10 (3):373 – 384.
    Horgan and Tienson do a wonderful job of explicating the dynamical system perspective and contrasting that view with classical AI approaches. However, their arguments for replacing a classical conception of connectionism with system dynamics rely on philosophical distinctions that do not make a difference. In particular, (1) their generalized version of Man's three levels of analysis collapses into itself; (2) their description of attractor dynamics works better than their metaphor of forces; and (3) their versions of “soft laws” and physical (...)
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  • Color for pigeons and philosophers.C. L. Hardin - 1992 - Behavioral and Brain Sciences 15 (1):37-38.
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  • Optimization and simplicity: Computational vision and biological explanation.Daniel J. Gilman - 1996 - Synthese 107 (3):293 - 323.
    David Marr's theory of vision has been a rich source of inspiration, fascination and confusion. I will suggest that some of this confusion can be traced to discrepancies between the way Marr developed his theory in practice and the way he suggested such a theory ought to be developed in his explicit metatheoretical remarks. I will address claims that Marr's theory may be seen as an optimizing theory, along with the attendant suggestion that optimizing assumptions may be inappropriate for cognitive (...)
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  • On the neural enrichment of economic models: recasting the challenge.Roberto Fumagalli - 2017 - Biology and Philosophy 32 (2):201-220.
    In a recent article in this Journal, Fumagalli argues that economists are provisionally justified in resisting prominent calls to integrate neural variables into economic models of choice. In other articles, various authors engage with Fumagalli’s argument and try to substantiate three often-made claims concerning neuroeconomic modelling. First, the benefits derivable from neurally informing some economic models of choice do not involve significant tractability costs. Second, neuroeconomic modelling is best understood within Marr’s three-level of analysis framework for information-processing systems. And third, (...)
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  • Psychophysical modeling: The link between objectivism and subjectivism.Marcia A. Finkelstein - 1992 - Behavioral and Brain Sciences 15 (1):36-37.
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  • Enactivist vision.Jerome A. Feldman - 1992 - Behavioral and Brain Sciences 15 (1):35-36.
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  • What is a colour space?Jules Davidoff - 1992 - Behavioral and Brain Sciences 15 (1):34-35.
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