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  1. What to Say to a Skeptical Metaphysician? A Defense Manual for Cognitive and Behavioral Scientists.Don Ross & David Spurrett - 2004 - Behavioral and Brain Sciences 27 (5):603-627.
    A wave of recent work in metaphysics seeks to undermine the anti-reductionist, functionalist consensus of the past few decades in cognitive science and philosophy of mind. That consensus apparently legitimated a focus on what systems do, without necessarily and always requiring attention to the details of how systems are constituted. The new metaphysical challenge contends that many states and processes referred to by functionalist cognitive scientists are epiphenomenal. It further contends that the problem lies in functionalism itself, and that, to (...)
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  • How to Be a Successful Scientist.Paul Thagard - 2005 - In M. Gorman, R. Tweney, D. Gooding & A. Kincannon (eds.), Scientific and Technological Thinking. Erlbaum. pp. 159--171.
  • On Levels of Cognitive Modeling.Ron Sun, Andrew Coward & Michael J. Zenzen - 2005 - Philosophical Psychology 18 (5):613-637.
    The article first addresses the importance of cognitive modeling, in terms of its value to cognitive science (as well as other social and behavioral sciences). In particular, it emphasizes the use of cognitive architectures in this undertaking. Based on this approach, the article addresses, in detail, the idea of a multi-level approach that ranges from social to neural levels. In physical sciences, a rigorous set of theories is a hierarchy of descriptions/explanations, in which causal relationships among entities at a high (...)
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  • Criteria for an Effective Theory of Consciousness and Some Preliminary Attempts.Ron Sun - 2004 - Consciousness and Cognition 13 (2):268-301.
    In the physical sciences a rigorous theory is a hierarchy of descriptions in which causal relationships between many general types of entity at a phenomenological level can be derived from causal relationships between smaller numbers of simpler entities at more detailed levels. The hierarchy of descriptions resembles the modular hierarchy created in electronic systems in order to be able to modify a complex functionality without excessive side effects. Such a hierarchy would make it possible to establish a rigorous scientific theory (...)
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  • Explanation: A Mechanist Alternative.William Bechtel & Adele Abrahamsen - 2005 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 36 (2):421-441.
  • Explanation: A Mechanist Alternative.William Bechtel - 2005 - Studies in History and Philosophy of Biol and Biomed Sci 36 (2):421--441.
    Explanations in the life sciences frequently involve presenting a model of the mechanism taken to be responsible for a given phenomenon. Such explanations depart in numerous ways from nomological explanations commonly presented in philosophy of science. This paper focuses on three sorts of differences. First, scientists who develop mechanistic explanations are not limited to linguistic representations and logical inference; they frequently employ dia- grams to characterize mechanisms and simulations to reason about them. Thus, the epistemic resources for presenting mechanistic explanations (...)
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  • The Scientific Image.Bas C. Van Fraassen - 1980 - Journal of Philosophy 79 (5):274-283.
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  • Explanatory Unification.Philip Kitcher - 1981 - Philosophy of Science 48 (4):507-531.
    The official model of explanation proposed by the logical empiricists, the covering law model, is subject to familiar objections. The goal of the present paper is to explore an unofficial view of explanation which logical empiricists have sometimes suggested, the view of explanation as unification. I try to show that this view can be developed so as to provide insight into major episodes in the history of science, and that it can overcome some of the most serious difficulties besetting the (...)
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  • Aspects of Scientific Explanation.Carl G. Hempel - 1965 - Journal of Symbolic Logic 37 (4):747-749.
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  • Implicit Learning and Tacit Knowledge.Arthur S. Reber - 1989 - Journal of Experimental Psychology: General 118 (3):219-235.
    I examine the phenomenon of implicit learning, the process by which knowledge about the rule-governed complexities of the stimulus environment is acquired independently of conscious attempts to do so. Our research with the two seemingly disparate experimental paradigms of synthetic grammar learning and probability learning, is reviewed and integrated with other approaches to the general problem of unconscious cognition. The conclusions reached are as follows: Implicit learning produces a tacit knowledge base that is abstract and representative of the structure of (...)
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  • From Implicit Skills to Explicit Knowledge: A Bottom‐Up Model of Skill Learning.Edward Merrillb & Todd Petersonb - 2001 - Cognitive Science 25 (2):203-244.
  • Accounting for the Computational Basis of Consciousness: A Connectionist Approach.Ron Sun - 1999 - Consciousness and Cognition 8 (4):529-565.
    This paper argues for an explanation of the mechanistic (computational) basis of consciousness that is based on the distinction between localist (symbolic) representation and distributed representation, the ideas of which have been put forth in the connectionist literature. A model is developed to substantiate and test this approach. The paper also explores the issue of the functional roles of consciousness, in relation to the proposed mechanistic explanation of consciousness. The model, embodying the representational difference, is able to account for the (...)
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  • Thinking About Mechanisms.Peter K. Machamer, Lindley Darden & Carl F. Craver - 2000 - Philosophy of Science 67 (1):1-25.
    The concept of mechanism is analyzed in terms of entities and activities, organized such that they are productive of regular changes. Examples show how mechanisms work in neurobiology and molecular biology. Thinking in terms of mechanisms provides a new framework for addressing many traditional philosophical issues: causality, laws, explanation, reduction, and scientific change.
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  • How Persuasive is a Good Fit? A Comment on Theory Testing.Seth Roberts & Harold Pashler - 2000 - Psychological Review 107 (2):358-367.
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  • Desiderata for Cognitive Architectures.Ron Sun - 2004 - Philosophical Psychology 17 (3):341-373.
    This article addresses issues in developing cognitive architectures--generic computational models of cognition. Cognitive architectures are believed to be essential in advancing understanding of the mind, and therefore, developing cognitive architectures is an extremely important enterprise in cognitive science. The article proposes a set of essential desiderata for developing cognitive architectures. It then moves on to discuss in detail some of these desiderata and their associated concepts and ideas relevant to developing better cognitive architectures. It argues for the importance of taking (...)
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  • How Models Are Used to Represent Reality.Ronald N. Giere - 2002 - Philosophy of Science 71 (5):742-752.
    Most recent philosophical thought about the scientific representation of the world has focused on dyadic relationships between language-like entities and the world, particularly the semantic relationships of reference and truth. Drawing inspiration from diverse sources, I argue that we should focus on the pragmatic activity of representing, so that the basic representational relationship has the form: Scientists use models to represent aspects of the world for specific purposes. Leaving aside the terms "law" and "theory," I distinguish principles, specific conditions, models, (...)
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  • The Interaction of the Explicit and the Implicit in Skill Learning: A Dual-Process Approach.Ron Sun - 2005 - Psychological Review 112 (1):159-192.
    This article explicates the interaction between implicit and explicit processes in skill learning, in contrast to the tendency of researchers to study each type in isolation. It highlights various effects of the interaction on learning (including synergy effects). The authors argue for an integrated model of skill learning that takes into account both implicit and explicit processes. Moreover, they argue for a bottom-up approach (first learning implicit knowledge and then explicit knowledge) in the integrated model. A variety of qualitative data (...)
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  • Physical Symbol Systems.Allen Newell - 1980 - Cognitive Science 4 (2):135-83.
  • Models: The Blueprints for Laws.Nancy Cartwright - 1997 - Philosophy of Science 64 (4):303.
    In this paper the claim that laws of nature are to be understood as claims about what necessarily or reliably happens is disputed. Laws can characterize what happens in a reliable way, but they do not do this easily. We do not have laws for everything occurring in the world, but only for those situations where what happens in nature is represented by a model: models are blueprints for nomological machines, which in turn give rise to laws. An example from (...)
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  • Computing Machinery and Intelligence.Alan M. Turing - 1950 - Mind 59 (October):433-60.
    I propose to consider the question, "Can machines think?" This should begin with definitions of the meaning of the terms "machine" and "think." The definitions might be framed so as to reflect so far as possible the normal use of the words, but this attitude is dangerous, If the meaning of the words "machine" and "think" are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning and the answer to (...)
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  • Unified Theories of Cognition.Allen Newell - 1990 - Harvard University Press.
    In this book, Newell makes the case for unified theories by setting forth a candidate.
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  • Models as Mediators: Perspectives on Natural and Social Science.Mary S. Morgan & Margaret Morrison (eds.) - 1999 - Cambridge University Press.
    Models as Mediators discusses the ways in which models function in modern science, particularly in the fields of physics and economics. Models play a variety of roles in the sciences: they are used in the development, exploration and application of theories and in measurement methods. They also provide instruments for using scientific concepts and principles to intervene in the world. The editors provide a framework which covers the construction and function of scientific models, and explore the ways in which they (...)
     
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  • Scientific Explanation and the Causal Structure of the World.Ronald N. Giere & Wesley C. Salmon - 1988 - Philosophical Review 97 (3):444.
  • The Structure of Scientific Theories.Frederick Suppe - 1979 - Philosophy and Phenomenological Research 40 (1):148-150.
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  • The Philosophy of Science.Michael Scriven & Stephen Toulmin - 1955 - Philosophical Review 64 (1):124.
  • Causality and Explanation.Wesley Salmon - 2000 - Erkenntnis 52 (1):121-125.
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  • 1999.M. Morrison & M. Morgan - 1999 - In Margaret Morrison & Mary Morgan (eds.), Models as Mediators: Perspectives on Natural and Social Science.
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  • Discovering Complexity Decomposition and Localization as Strategies in Scientific Research.William Bechtel & Robert C. Richardson - 1993 - Princeton.
     
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  • Philosophy of Mind: An Overview for Cognitive Science.William P. Bechtel - 1988 - Lawrence Erlbaum.
    Specifically designed to make the philosophy of mind intelligible to those not trained in philosophy, this book provides a concise overview for students and researchers in the cognitive sciences. Emphasizing the relevance of philosophical work to investigations in other cognitive sciences, this unique text examines such issues as the meaning of language, the mind-body problem, the functionalist theories of cognition, and intentionality. As he explores the philosophical issues, Bechtel draws connections between philosophical views and theoretical and experimental work in such (...)
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  • Scientific Explanation and the Causal Structure of the World.Wesley Salmon - 1984 - Princeton University Press.
    The philosophical theory of scientific explanation proposed here involves a radically new treatment of causality that accords with the pervasively statistical character of contemporary science. Wesley C. Salmon describes three fundamental conceptions of scientific explanation--the epistemic, modal, and ontic. He argues that the prevailing view is untenable and that the modal conception is scientifically out-dated. Significantly revising aspects of his earlier work, he defends a causal/mechanical theory that is a version of the ontic conception. Professor Salmon's theory furnishes a robust (...)
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  • Computer Science as Empirical Inquiry: Symbols and Search.Allen Newell & Herbert A. Simon - 1981 - Communications of the Association for Computing Machinery 19:113-26.
  • A Framework for Representing Knowledge.Marvin Minsky - unknown
    It seems to me that the ingredients of most theories both in Artificial Intelligence and in Psychology have been on the whole too minute, local, and unstructured to account–either practically or phenomenologically–for the effectiveness of common-sense thought. The "chunks" of reasoning, language, memory, and "perception" ought to be larger and more structured; their factual and procedural contents must be more intimately connected in order to explain the apparent power and speed of mental activities.
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  • Scientific Explanation.P. Kitcher & W. C. Salmon - 1992 - British Journal for the Philosophy of Science 43 (1):85-98.
     
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  • The Philosophy of Science.Stephen Toulmin - 1954 - Mind 63 (251):403-412.
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  • The Structure of Scientific Theories.Frederick Suppe - 1979 - Critica 11 (31):138-140.
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  • The Society of Mind.Marvin Minsky - 1987 - The Personalist Forum 3 (1):19-32.
  • On Levels of Cognitive Modeling.Ron Sun, L. Andrew Coward & Michael J. Zenzen - 2005 - Philosophical Psychology 18 (5):613-637.
  • Metacognition: Core Readings.T. O. Nelson - 1992 - Allyn & Bacon.
  • Scientific Explanation.Philip Kitcher & Wesley Salmon (eds.) - 1989 - Univ of Minnesota Pr.
    Studdert-Kennedy, Gerald, Evidence and Explanation in Social Science. ... Kauffman, Stuart, "Articulation of Parts Explanation in Biology and the Rational ...
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  • You Can't Play 20 Questions with Nature and Win: Projective Comments on the Papers of This Symposium.Allen Newell - 1973 - Computer Science Department.
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  • Computational Models in the Philosophy of Science.Paul Thagard - 1986 - PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1986:329 - 335.
    Computational models can aid in the development of philosophical views concerning the structure and growth of scientific knowledge. In cognitive psychology, computational models have proved valuable for describing the structures and processes of thought and for testing these models by writing and running computer programs using the techniques of artificial intelligence. Similarly, in the philosophy of science models can be developed that shed light on the structure, discovery, and justification of scientific theories. This paper briefly describes a computational model of (...)
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  • Structure of Scientific Revolutions, the (Ch. 9 Only).Thomas Kuhn - unknown
  • Constraining Computational Models of Cognition.Terry Regier - 2003 - In L. Nadel (ed.), Encyclopedia of Cognitive Science. Nature Publishing Group. pp. 611--615.
  • The Society Of Mind.Marvin L. Minsky - 1986 - Simon & Schuster.
  • The Cambridge Handbook of Computational Psychology.Ron Sun (ed.) - 2008 - Cambridge University Press.
    This book is a definitive reference source for the growing, increasingly more important, and interdisciplinary field of computational cognitive modeling, that is, computational psychology. It combines breadth of coverage with definitive statements by leading scientists in this field. Research in computational cognitive modeling explores the essence of cognition through developing detailed, process-based understanding by specifying computational mechanisms, structures, and processes. Computational models provide both conceptual clarity and precision at the same time. This book substantiates this approach through overviews and many (...)
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  • The Evolution of Cooperation.Robert Axelrod - 1984 - Basic Books.
    The 'Evolution of Cooperation' addresses a simple yet age-old question; If living things evolve through competition, how can cooperation ever emerge? Despite the abundant evidence of cooperation all around us, there existed no purely naturalistic answer to this question until 1979, when Robert Axelrod famously ran a computer tournament featuring a standard game-theory exercise called The Prisoner's Dilemma. To everyone's surprise, the program that won the tournament, named Tit for Tat, was not only the simplest but the most "cooperative" entrant. (...)
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  • Computational Cognitive Modeling the Source of Power and Other Related Issues.Ron Sun - unknown
    Computational cognitive models hypothesize internal mental processes of human cognitive activities and express such activities by computer programs Such computational models often consist of many components and aspects Claims are often made that certain aspects of the models play a key role in modeling but such claims are sometimes not well justi ed or explored In this paper we rst review some fundamental distinctions and issues in computational modeling We then discuss in principle systematic ways of identifying the source of (...)
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  • Duality of the Mind.Ron Sun - manuscript
    Synthesizing situated cognition, reinforcement learning, and hybrid connectionist modeling, a generic cognitive architecture focused on situated involvement and interaction with the world is developed in this book. The architecture notably incorporates the distinction of implicit and explicit processes.
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  • Simulating Organizational Decision-Making Using a Cognitively Realistic Agent Model.Ron Sun - manuscript
    Most of the work in agent-based social simulation has assumed highly simplified agent models, with little attention being paid to the details of individual cognition. Here, in an effort to counteract that trend, we substitute a realistic cognitive agent model (CLARION) for the simpler models previously used in an organizational design task. On that basis, an exploration is made of the interaction between the cognitive parameters that govern individual agents, the placement of agents in different organizational structures, and the performance (...)
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  • Top-Down Versus Bottom-Up Learning in Cognitive Skill Acquisition.Ron Sun - unknown
    This paper explores the interaction between implicit and explicit processes during skill learning, in terms of top-down learning (that is, learning that goes from explicit to implicit knowledge) versus bottom-up learning (that is, learning that goes from implicit to explicit knowledge). Instead of studying each type of knowledge (implicit or explicit) in isolation, we stress the interaction between the two types, especially in terms of one type giving rise to the other, and its effects on learning. The work presents an (...)
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