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  1. Philosophy and Memory Traces: Descartes to Connectionism.John Sutton - 1998 - New York: Cambridge University Press.
    Philosophy and Memory Traces defends two theories of autobiographical memory. One is a bewildering historical view of memories as dynamic patterns in fleeting animal spirits, nervous fluids which rummaged through the pores of brain and body. The other is new connectionism, in which memories are 'stored' only superpositionally, and reconstructed rather than reproduced. Both models, argues John Sutton, depart from static archival metaphors by employing distributed representation, which brings interference and confusion between memory traces. Both raise urgent issues about control (...)
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  • Rational analysis, intractability, and the prospects of ‘as if’-explanations.Iris van Rooij, Johan Kwisthout, Todd Wareham & Cory Wright - 2018 - Synthese 195 (2):491-510.
    Despite their success in describing and predicting cognitive behavior, the plausibility of so-called ‘rational explanations’ is often contested on the grounds of computational intractability. Several cognitive scientists have argued that such intractability is an orthogonal pseudoproblem, however, since rational explanations account for the ‘why’ of cognition but are agnostic about the ‘how’. Their central premise is that humans do not actually perform the rational calculations posited by their models, but only act as if they do. Whether or not the problem (...)
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  • Neural networks, nativism, and the plausibility of constructivism.Steven R. Quartz - 1993 - Cognition 48 (3):223-242.
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  • Connectionist models learn what?Timothy van Gelder - 1990 - Behavioral and Brain Sciences 13 (3):509-510.
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  • A non-empiricist perspective on learning in layered networks.Michael I. Jordan - 1990 - Behavioral and Brain Sciences 13 (3):497-498.
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  • Modularity need not imply locality: Damaged modules can have nonlocal effects.Edgar Zurif & David Swinney - 1994 - Behavioral and Brain Sciences 17 (1):89-90.
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  • Modularity, abstractness and the interactive brain.James M. Clark - 1994 - Behavioral and Brain Sciences 17 (1):67-68.
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  • Do neuropsychologists think in terms of interactive models?Marcel Kinsbourne - 1994 - Behavioral and Brain Sciences 17 (1):72-73.
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  • (1 other version)Connectionist Natural Language Processing: The State of the Art.Morten H. Christiansen & Nick Chater - 1999 - Cognitive Science 23 (4):417-437.
    This Special Issue on Connectionist Models of Human Language Processing provides an opportunity for an appraisal both of specific connectionist models and of the status and utility of connectionist models of language in general. This introduction provides the background for the papers in the Special Issue. The development of connectionist models of language is traced, from their intellectual origins, to the state of current research. Key themes that arise throughout different areas of connectionist psycholinguistics are highlighted, and recent developments in (...)
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  • Précis of simple heuristics that make us Smart.Peter M. Todd & Gerd Gigerenzer - 2000 - Behavioral and Brain Sciences 23 (5):727-741.
    How can anyone be rational in a world where knowledge is limited, time is pressing, and deep thought is often an unattainable luxury? Traditional models of unbounded rationality and optimization in cognitive science, economics, and animal behavior have tended to view decision-makers as possessing supernatural powers of reason, limitless knowledge, and endless time. But understanding decisions in the real world requires a more psychologically plausible notion of bounded rationality. In Simple heuristics that make us smart (Gigerenzer et al. 1999), we (...)
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  • Connectionism, classical cognitive science and experimental psychology.Mike Oaksford, Nick Chater & Keith Stenning - 1990 - AI and Society 4 (1):73-90.
    Classical symbolic computational models of cognition are at variance with the empirical findings in the cognitive psychology of memory and inference. Standard symbolic computers are well suited to remembering arbitrary lists of symbols and performing logical inferences. In contrast, human performance on such tasks is extremely limited. Standard models donot easily capture content addressable memory or context sensitive defeasible inference, which are natural and effortless for people. We argue that Connectionism provides a more natural framework in which to model this (...)
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  • Towards a connectionist cognitive architecture.Keith Butler - 1991 - Mind and Language 6 (3):252-72.
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  • A rational analysis of the selection task as optimal data selection.Mike Oaksford & Nick Chater - 1994 - Psychological Review 101 (4):608-631.
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  • Probabilistic single function dual process theory and logic programming as approaches to non-monotonicity in human vs. artificial reasoning.Mike Oaksford & Nick Chater - 2014 - Thinking and Reasoning 20 (2):269-295.
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  • Keeping representations at bay.Stanley Munsat - 1990 - Behavioral and Brain Sciences 13 (3):502-503.
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  • Representational systems and symbolic systems.Gordon D. A. Brown & Mike Oaksford - 1990 - Behavioral and Brain Sciences 13 (3):492-493.
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  • Perception and its interactive substrate: Psychophysical linking hypotheses and psychophysical methods.Robert Sekuler - 1994 - Behavioral and Brain Sciences 17 (1):79-79.
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  • Prosopagnosia, conscious awareness and the interactive brain.Robert Van Gulick - 1994 - Behavioral and Brain Sciences 17 (1):84-85.
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  • Clarifying the locality assumption.Clark Glymour - 1994 - Behavioral and Brain Sciences 17 (1):69-70.
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  • Toward a Connectionist Model of Recursion in Human Linguistic Performance.Morten H. Christiansen & Nick Chater - 1999 - Cognitive Science 23 (2):157-205.
    Naturally occurring speech contains only a limited amount of complex recursive structure, and this is reflected in the empirically documented difficulties that people experience when processing such structures. We present a connectionist model of human performance in processing recursive language structures. The model is trained on simple artificial languages. We find that the qualitative performance profile of the model matches human behavior, both on the relative difficulty of center‐embedding and cross‐dependency, and between the processing of these complex recursive structures and (...)
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  • The imaginary fundamentalists: The unshocking truth about Bayesian cognitive science.Nick Chater, Noah Goodman, Thomas L. Griffiths, Charles Kemp, Mike Oaksford & Joshua B. Tenenbaum - 2011 - Behavioral and Brain Sciences 34 (4):194-196.
    If Bayesian Fundamentalism existed, Jones & Love's (J&L's) arguments would provide a necessary corrective. But it does not. Bayesian cognitive science is deeply concerned with characterizing algorithms and representations, and, ultimately, implementations in neural circuits; it pays close attention to environmental structure and the constraints of behavioral data, when available; and it rigorously compares multiple models, both within and across papers. J&L's recommendation of Bayesian Enlightenment corresponds to past, present, and, we hope, future practice in Bayesian cognitive science.
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  • Against Logicist Cognitive Science.Mike Oaksford & Nick Chater - 1991 - Mind and Language 6 (1):1-38.
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  • Two projects for understanding the mind: A response to Morris and Richardson. [REVIEW]Nick Chater & Martin Pickering - 1997 - Minds and Machines 7 (4):553-569.
    We respond to Morris and Richardson 's claim that Pickering and Chater's arguments about the lack of a relation between cognitive science and folk psychology are flawed. We note that possible controversies about the appropriate uses for the two terms do not affect our arguments. We then address their claim that computational explanation of knowledge-rich processes has proved possible in the domains of problem solving, scientific discovery, and reasoning. We argue that, in all cases, computational explanation is only possible for (...)
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  • The rational analysis of mind and behavior.Nick Chater & Mike Oaksford - 2000 - Synthese 122 (1-2):93-131.
    Rational analysis (Anderson 1990, 1991a) is an empiricalprogram of attempting to explain why the cognitive system isadaptive, with respect to its goals and the structure of itsenvironment. We argue that rational analysis has two importantimplications for philosophical debate concerning rationality. First,rational analysis provides a model for the relationship betweenformal principles of rationality (such as probability or decisiontheory) and everyday rationality, in the sense of successfulthought and action in daily life. Second, applying the program ofrational analysis to research on human reasoning (...)
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  • Interactions on the interactive brain.Martha J. Farah - 1994 - Behavioral and Brain Sciences 17 (1):90-104.
    When cognitive neuropsychologists make inferences about the functional architecture of the normal mind from selective cognitive impairments they generally assume that the effects of brain damage are local, that is, that the nondamaged components of the architecture continue to function as they did before the damage. This assumption follows from the view that the components of the functional architecture are modular, in the sense of being informationally encapsulated. In this target article it is argued that this “locality” assumption is probably (...)
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  • What counts as local?Andrew W. Young - 1994 - Behavioral and Brain Sciences 17 (1):88-89.
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  • Modularity, interaction and connectionist neuropsychology.Nick Chater - 1994 - Behavioral and Brain Sciences 17 (1):66-67.
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  • The Role of Falsification in the Development of Cognitive Architectures: Insights from a Lakatosian Analysis.Richard P. Cooper - 2007 - Cognitive Science 31 (3):509-533.
    It has been suggested that the enterprise of developing mechanistic theories of the human cognitive architecture is flawed because the theories produced are not directly falsifiable. Newell attempted to sidestep this criticism by arguing for a Lakatosian model of scientific progress in which cognitive architectures should be understood as theories that develop over time. However, Newell's own candidate cognitive architecture adhered only loosely to Lakatosian principles. This paper reconsiders the role of falsification and the potential utility of Lakatosian principles in (...)
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  • Connectionism and classical computation.Nick Chater - 1990 - Behavioral and Brain Sciences 13 (3):493-494.
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  • Advances in neural network theory.Gérard Toulouse - 1990 - Behavioral and Brain Sciences 13 (3):509-509.
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  • Neuropsychological inference with an interactive brain: A critique of the “locality” assumption.Martha J. Farah - 1994 - Behavioral and Brain Sciences 17 (1):43-61.
    When cognitive neuropsychologists make inferences about the functional architecture of the normal mind from selective cognitive impairments they generally assume that the effects of brain damage are local, that is, that the nondamaged components of the architecture continue to function as they did before the damage. This assumption follows from the view that the components of the functional architecture are modular, in the sense of being informationally encapsulated. In this target article it is argued that this “locality” assumption is probably (...)
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  • Is logicist cognitive science possible?Alan Garnham - 1993 - Mind and Language 8 (1):49-71.
    This paper argues against Oaksford and Chater's claim that logicist cognitive science is not possible. It suggests that there arguments against logicist cognitive science are too closely tied to the account of Pylyshyn and of Fodor, and that the correct way of thinking about logicist cognitive science is in a mental models framework.
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  • The symbolic brain or the invisible hand?René van Hezewijk & Edward H. F. de Haan - 1994 - Behavioral and Brain Sciences 17 (1):85-86.
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  • Computational levels again.Mike Oaksford - 1994 - Behavioral and Brain Sciences 17 (1):76-77.
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  • Learning and representation: Tensions at the interface.Steven José Hanson - 1990 - Behavioral and Brain Sciences 13 (3):511-518.
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  • Neuropsychology: Going loco?Rosaleen A. McCarthy - 1994 - Behavioral and Brain Sciences 17 (1):73-74.
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  • Distributed locality and large-scale neurocognitive networks.M. Marsel Mesulam - 1994 - Behavioral and Brain Sciences 17 (1):74-76.
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  • The Allure of Connectionism Reexamined.B. P. McLaughlin & T. A. Warfield - 1994 - Synthese 101 (3):365 - 400.
    There is currently a debate over whether cognitive architecture is classical or connectionist in nature. One finds the following three comparisons between classical architecture and connectionist architecture made in the pro-connectionist literature in this debate: (1) connectionist architecture is neurally plausible and classical architecture is not; (2) connectionist architecture is far better suited to model pattern recognition capacities than is classical architecture; and (3) connectionist architecture is far better suited to model the acquisition of pattern recognition capacities by learning than (...)
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  • Theories of reasoning and the computational explanation of everyday inference.Mike Oaksford & Nick Chater - 1995 - Thinking and Reasoning 1 (2):121 – 152.
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  • Approaches to learning and representation.Pat Langley - 1990 - Behavioral and Brain Sciences 13 (3):500-501.
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  • Neurocomputing and modularity.Joachim Diederich - 1994 - Behavioral and Brain Sciences 17 (1):68-69.
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  • Classical and connectionist models: Levels of description.Josep E. Corbí - 1993 - Synthese 95 (2):141 - 168.
    To begin, I introduce an analysis of interlevel relations that allows us to offer an initial characterization of the debate about the way classical and connectionist models relate. Subsequently, I examine a compatibility thesis and a conditional claim on this issue.With respect to the compatibility thesis, I argue that, even if classical and connectionist models are not necessarily incompatible, the emergence of the latter seems to undermine the best arguments for the Language of Thought Hypothesis, which is essential to the (...)
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  • The Computational Origin of Representation.Steven T. Piantadosi - 2020 - Minds and Machines 31 (1):1-58.
    Each of our theories of mental representation provides some insight into how the mind works. However, these insights often seem incompatible, as the debates between symbolic, dynamical, emergentist, sub-symbolic, and grounded approaches to cognition attest. Mental representations—whatever they are—must share many features with each of our theories of representation, and yet there are few hypotheses about how a synthesis could be possible. Here, I develop a theory of the underpinnings of symbolic cognition that shows how sub-symbolic dynamics may give rise (...)
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  • How connectionist models learn: The course of learning in connectionist networks.John K. Kruschke - 1990 - Behavioral and Brain Sciences 13 (3):498-499.
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  • Toward a unification of conditioning and cognition in animal learning.William S. Maki - 1990 - Behavioral and Brain Sciences 13 (3):501-502.
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  • Regional specialities.Brian Butterworth - 1994 - Behavioral and Brain Sciences 17 (1):63-63.
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  • Early lexical influences on sublexical processing in speech perception: Evidence from electrophysiology.Colin Noe & Simon Fischer-Baum - 2020 - Cognition 197 (C):104162.
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  • What connectionist models learn: Learning and representation in connectionist networks.Stephen José Hanson & David J. Burr - 1990 - Behavioral and Brain Sciences 13 (3):471-489.
    Connectionist models provide a promising alternative to the traditional computational approach that has for several decades dominated cognitive science and artificial intelligence, although the nature of connectionist models and their relation to symbol processing remains controversial. Connectionist models can be characterized by three general computational features: distinct layers of interconnected units, recursive rules for updating the strengths of the connections during learning, and “simple” homogeneous computing elements. Using just these three features one can construct surprisingly elegant and powerful models of (...)
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  • The allure of connectionism reexamined.Brian P. McLaughlin & F. Warfield - 1994 - Synthese 101 (3):365-400.
    There is currently a debate over whether cognitive architecture is classical or connectionist in nature. One finds the following three comparisons between classical architecture and connectionist architecture made in the pro-connectionist literature in this debate: (1) connectionist architecture is neurally plausible and classical architecture is not; (2) connectionist architecture is far better suited to model pattern recognition capacities than is classical architecture; and (3) connectionist architecture is far better suited to model the acquisition of pattern recognition capacities by learning than (...)
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  • Locus-pocus.Carlo Semenza - 1994 - Behavioral and Brain Sciences 17 (1):80-80.
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