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  1. Bayesian Fundamentalism or Enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition.Matt Jones & Bradley C. Love - 2011 - Behavioral and Brain Sciences 34 (4):169-188.
    The prominence of Bayesian modeling of cognition has increased recently largely because of mathematical advances in specifying and deriving predictions from complex probabilistic models. Much of this research aims to demonstrate that cognitive behavior can be explained from rational principles alone, without recourse to psychological or neurological processes and representations. We note commonalities between this rational approach and other movements in psychology – namely, Behaviorism and evolutionary psychology – that set aside mechanistic explanations or make use of optimality assumptions. Through (...)
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  • Falsificationism and the Methodology of Scientific Research Programs' in I. Lakatos and A. Musgrave.Imre Lakatos - 1970 - In Imre Lakatos & Alan Musgrave (eds.), Criticism and the growth of knowledge. Cambridge [Eng.]: Cambridge University Press.
  • A Mathematical Theory of Communication.Claude Elwood Shannon - 1948 - Bell System Technical Journal 27 (April 1924):379–423.
    The mathematical theory of communication.
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  • Word learning as Bayesian inference.Fei Xu & Joshua B. Tenenbaum - 2007 - Psychological Review 114 (2):245-272.
  • Generalization, similarity, and bayesian inference.Joshua B. Tenenbaum & Thomas L. Griffiths - 2001 - Behavioral and Brain Sciences 24 (4):629-640.
    Shepard has argued that a universal law should govern generalization across different domains of perception and cognition, as well as across organisms from different species or even different planets. Starting with some basic assumptions about natural kinds, he derived an exponential decay function as the form of the universal generalization gradient, which accords strikingly well with a wide range of empirical data. However, his original formulation applied only to the ideal case of generalization from a single encountered stimulus to a (...)
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  • Rational approximations to rational models: Alternative algorithms for category learning.Adam N. Sanborn, Thomas L. Griffiths & Daniel J. Navarro - 2010 - Psychological Review 117 (4):1144-1167.
  • Distinguishing literal from metaphorical applications of Bayesian approaches.Timothy T. Rogers & Mark S. Seidenberg - 2011 - Behavioral and Brain Sciences 34 (4):211-212.
    We distinguish between literal and metaphorical applications of Bayesian models. When intended literally, an isomorphism exists between the elements of representation assumed by the rational analysis and the mechanism that implements the computation. Thus, observation of the implementation can externally validate assumptions underlying the rational analysis. In other applications, no such isomorphism exists, so it is not clear how the assumptions that allow a Bayesian model to fit data can be independently validated.
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  • The probabilistic approach to human reasoning.Mike Oaksford & Nick Chater - 2001 - Trends in Cognitive Sciences 5 (8):349-357.
    A recent development in the cognitive science of reasoning has been the emergence of a probabilistic approach to the behaviour observed on ostensibly logical tasks. According to this approach the errors and biases documented on these tasks occur because people import their everyday uncertain reasoning strategies into the laboratory. Consequently participants' apparently irrational behaviour is the result of comparing it with an inappropriate logical standard. In this article, we contrast the probabilistic approach with other approaches to explaining rationality, and then (...)
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  • The Bayesian reader: Explaining word recognition as an optimal Bayesian decision process.Dennis Norris - 2006 - Psychological Review 113 (2):327-357.
  • The knowledge level.Allen Newell - 1982 - Artificial Intelligence 18 (1):81-132.
  • The Algorithmic Level Is the Bridge Between Computation and Brain.Bradley C. Love - 2015 - Topics in Cognitive Science 7 (2):230-242.
    Every scientist chooses a preferred level of analysis and this choice shapes the research program, even determining what counts as evidence. This contribution revisits Marr's three levels of analysis and evaluates the prospect of making progress at each individual level. After reviewing limitations of theorizing within a level, two strategies for integration across levels are considered. One is top–down in that it attempts to build a bridge from the computational to algorithmic level. Limitations of this approach include insufficient theoretical constraint (...)
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  • Computational Rationality: Linking Mechanism and Behavior Through Bounded Utility Maximization.Richard L. Lewis, Andrew Howes & Satinder Singh - 2014 - Topics in Cognitive Science 6 (2):279-311.
    We propose a framework for including information‐processing bounds in rational analyses. It is an application of bounded optimality (Russell & Subramanian, 1995) to the challenges of developing theories of mechanism and behavior. The framework is based on the idea that behaviors are generated by cognitive mechanisms that are adapted to the structure of not only the environment but also the mind and brain itself. We call the framework computational rationality to emphasize the incorporation of computational mechanism into the definition of (...)
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  • Criticism and the Growth of Knowledge.Hugh Lehman - 1972 - Philosophy of Science 39 (1):92-95.
  • Criticism and the growth of knowledge.Imre Lakatos & Alan Musgrave (eds.) - 1970 - Cambridge [Eng.]: Cambridge University Press.
    Two books have been particularly influential in contemporary philosophy of science: Karl R. Popper's Logic of Scientific Discovery, and Thomas S. Kuhn's Structure of Scientific Revolutions. Both agree upon the importance of revolutions in science, but differ about the role of criticism in science's revolutionary growth. This volume arose out of a symposium on Kuhn's work, with Popper in the chair, at an international colloquium held in London in 1965. The book begins with Kuhn's statement of his position followed by (...)
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  • Rational adaptation under task and processing constraints: Implications for testing theories of cognition and action.Andrew Howes, Richard L. Lewis & Alonso Vera - 2009 - Psychological Review 116 (4):717-751.
  • Interactive Effects of Explicit Emergent Structure: A Major Challenge for Cognitive Computational Modeling.Robert M. French & Elizabeth Thomas - 2015 - Topics in Cognitive Science 7 (2):206-216.
    David Marr's (1982) three‐level analysis of computational cognition argues for three distinct levels of cognitive information processing—namely, the computational, representational, and implementational levels. But Marr's levels are—and were meant to be—descriptive, rather than interactive and dynamic. For this reason, we suggest that, had Marr been writing today, he might well have gone even farther in his analysis, including the emergence of structure—in particular, explicit structure at the conceptual level—from lower levels, and the effect of explicit emergent structures on the level (...)
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  • On explanation in cognitive science: Competence, idealization, and the failure of the classical cascade.Bradley Franks - 1995 - British Journal for the Philosophy of Science 46 (4):475-502.
    underpinning of the cognitive sciences. I argue, however, that it often fails to provide adequate explanations, in particular in conjunction with competence theories. This failure originates in the idealizations in competence descriptions, which either ?block? the cascade, or produce a successful cascade which fails to explain cognition.
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  • Aspects of the Theory of Syntax.Ann S. Ferebee - 1965 - Journal of Symbolic Logic 35 (1):167.
  • The Nature of Psychological Explanation.Robert Van Gulick - 1986 - Philosophy of Science 53 (4):616-618.
  • The Nature of Psychological Explanation.Robert Cummins - 1983 - MIT Press.
    In exploring the nature of psychological explanation, this book looks at how psychologists theorize about the human ability to calculate, to speak a language and the like. It shows how good theorizing explains or tries to explain such abilities as perception and cognition. It recasts the familiar explanations of "intelligence" and "cognitive capacity" as put forward by philosophers such as Fodor, Dennett, and others in terms of a theory of explanation that makes established doctrine more intelligible to professionals and their (...)
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  • Aspects of the Theory of Syntax.Noam Chomsky - 1965 - Cambridge, MA, USA: MIT Press.
    Chomsky proposes a reformulation of the theory of transformational generative grammar that takes recent developments in the descriptive analysis of particular ...
  • Aspects of the Theory of Syntax.George Kimball Plochmann - 1967 - Philosophy and Phenomenological Research 28 (2):278-280.
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  • Ability, Breadth, and Parsimony in Computational Models of Higher‐Order Cognition.Nicholas L. Cassimatis, Paul Bello & Pat Langley - 2008 - Cognitive Science 32 (8):1304-1322.
    Computational models will play an important role in our understanding of human higher‐order cognition. How can a model's contribution to this goal be evaluated? This article argues that three important aspects of a model of higher‐order cognition to evaluate are (a) its ability to reason, solve problems, converse, and learn as well as people do; (b) the breadth of situations in which it can do so; and (c) the parsimony of the mechanisms it posits. This article argues that fits of (...)
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  • Networks in Cognitive Science.Andrea Baronchelli, Ramon Ferrer-I.-Cancho, Romualdo Pastor-Satorras, Nick Chater & Morten H. Christiansen - 2013 - Trends in Cognitive Sciences 17 (7):348-360.
  • Logic as Marr's Computational Level: Four Case Studies.Giosuè Baggio, Michiel Lambalgen & Peter Hagoort - 2015 - Topics in Cognitive Science 7 (2):287-298.
    We sketch four applications of Marr's levels-of-analysis methodology to the relations between logic and experimental data in the cognitive neuroscience of language and reasoning. The first part of the paper illustrates the explanatory power of computational level theories based on logic. We show that a Bayesian treatment of the suppression task in reasoning with conditionals is ruled out by EEG data, supporting instead an analysis based on defeasible logic. Further, we describe how results from an EEG study on temporal prepositions (...)
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  • Logic as Marr's Computational Level: Four Case Studies.Giosuè Baggio, Michiel van Lambalgen & Peter Hagoort - 2015 - Topics in Cognitive Science 7 (2):287-298.
    We sketch four applications of Marr's levels‐of‐analysis methodology to the relations between logic and experimental data in the cognitive neuroscience of language and reasoning. The first part of the paper illustrates the explanatory power of computational level theories based on logic. We show that a Bayesian treatment of the suppression task in reasoning with conditionals is ruled out by EEG data, supporting instead an analysis based on defeasible logic. Further, we describe how results from an EEG study on temporal prepositions (...)
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  • Using fMRI to Test Models of Complex Cognition.John R. Anderson, Cameron S. Carter, Jon M. Fincham, Yulin Qin, Susan M. Ravizza & Miriam Rosenberg-Lee - 2008 - Cognitive Science 32 (8):1323-1348.
    This article investigates the potential of fMRI to test assumptions about different components in models of complex cognitive tasks. If the components of a model can be associated with specific brain regions, one can make predictions for the temporal course of the BOLD response in these regions. An event‐locked procedure is described for dealing with temporal variability and bringing model runs and individual data trials into alignment. Statistical methods for testing the model are described that deal with the scan‐to‐scan correlations (...)
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  • Human symbol manipulation within an integrated cognitive architecture.John R. Anderson - 2005 - Cognitive Science 29 (3):313-341.
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  • Can Computational Goals Inform Theories of Vision?Barton L. Anderson - 2015 - Topics in Cognitive Science 7 (2):274-286.
    One of the most lasting contributions of Marr's posthumous book is his articulation of the different “levels of analysis” that are needed to understand vision. Although a variety of work has examined how these different levels are related, there is comparatively little examination of the assumptions on which his proposed levels rest, or the plausibility of the approach Marr articulated given those assumptions. Marr placed particular significance on computational level theory, which specifies the “goal” of a computation, its appropriateness for (...)
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  • Towards an Ontology of Cognitive Control.Agatha Lenartowicz, Donald J. Kalar, Eliza Congdon & Russell A. Poldrack - 2010 - Topics in Cognitive Science 2 (4):678-692.
  • The Mathematical Theory of Communication.Claude E. Shannon & Warren Weaver - 1949 - University of Illinois Press.
    Scientific knowledge grows at a phenomenal pace--but few books have had as lasting an impact or played as important a role in our modern world as The Mathematical Theory of Communication, published originally as a paper on communication theory more than fifty years ago. Republished in book form shortly thereafter, it has since gone through four hardcover and sixteen paperback printings. It is a revolutionary work, astounding in its foresight and contemporaneity. The University of Illinois Press is pleased and honored (...)
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  • Unified theories of cognition.Allen Newell - 1990 - Cambridge, Mass.: Harvard University Press.
    In this book, Newell makes the case for unified theories by setting forth a candidate.
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  • How Can the Human Mind Occur in the Physical Universe?John R. Anderson - 2007 - Oup Usa.
    The human cognitive architecture consists of a set of largely independent modules associated with different brain regions. This book discusses in detail how these various modules can combine to produce behaviours as varied as driving a car and solving an algebraic equation.
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  • The Organisation of Mind.Tim Shallice & Rick Cooper - 2011 - Oxford University Press.
    To understand the mind, we need to draw equally on the fields of cognitive science and neuroscience. But these two fields have very separate intellectual roots, and very different styles. So how can these two be reconciled in order to develop a full understanding of the mind and brain.This is the focus of this landmark new book.
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  • Bayesian Rationality: The Probabilistic Approach to Human Reasoning.Mike Oaksford & Nick Chater - 2007 - Oxford University Press.
    Are people rational? This question was central to Greek thought and has been at the heart of psychology and philosophy for millennia. This book provides a radical and controversial reappraisal of conventional wisdom in the psychology of reasoning, proposing that the Western conception of the mind as a logical system is flawed at the very outset. It argues that cognition should be understood in terms of probability theory, the calculus of uncertain reasoning, rather than in terms of logic, the calculus (...)
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  • Vision.David Marr - 1982 - W. H. Freeman.
  • The Architecture of Complexity.Herbert A. Simon - 1962 - Proceedings of the American Philosophical Society 106.
     
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