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  1. From meta-processes to conscious access: Evidence from children's metalinguistic and repair data.Annette Karmiloff-Smith - 1986 - Cognition 23 (2):95-147.
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  • A broader view of psychology and of computation.Gordon Pask - 1979 - Behavioral and Brain Sciences 2 (3):486-486.
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  • Is an ecological approach radical enough?H. C. Plotkin & F. J. Odling-Smee - 1981 - Behavioral and Brain Sciences 4 (1):154-155.
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  • An ecological theory of learning: Good goal, poor strategy.Sara J. Shettleworth - 1981 - Behavioral and Brain Sciences 4 (1):160-161.
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  • A theory of learning - not even déjà vu.George W. Barlow & Stephen E. Glickman - 1981 - Behavioral and Brain Sciences 4 (1):141-142.
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  • General process theory, ecology, and animal-human continuity: A cognitive perspective.Janet L. Lachman & Roy Lachman - 1981 - Behavioral and Brain Sciences 4 (1):149-150.
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  • A fourth approach to the study of learning: Are “processes” really necessary?John C. Malone - 1981 - Behavioral and Brain Sciences 4 (1):151-152.
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  • Contrasting approaches to a theory of learning.Timothy D. Johnston - 1981 - Behavioral and Brain Sciences 4 (1):125-139.
    The general process view of learning, which guided research into learning for the first half of this century, has come under attack in recent years from several quarters. One form of criticism has come from proponents of the so-called biological boundaries approach to learning. These theorists have presented a variety of data showing that supposedly general laws of learning may in fact be limited in their applicability to different species and learning tasks, and they argue that the limitations are drawn (...)
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  • Intentionality and communication theory.K. M. Sayre - 1986 - Behavioral and Brain Sciences 9 (1):155-165.
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  • Intentionality and information theory.David P. Ellerman - 1986 - Behavioral and Brain Sciences 9 (1):143-144.
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  • A total process approach to perception.Maxine Morphis - 1986 - Behavioral and Brain Sciences 9 (1):150-151.
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  • Direct perception and perceptual processes.Gunnar Johansson, Claes von Hofsten & Gunnar Jansson - 1980 - Behavioral and Brain Sciences 3 (3):388-388.
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  • Difficulties with a direct theory of perception.Irvin Rock - 1980 - Behavioral and Brain Sciences 3 (3):398-399.
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  • The Computer Revolution in Philosophy: Philosophy, Science, and Models of Mind.Aaron Sloman - 1978 - Hassocks UK: Harvester Press.
    Extract from Hofstadter's revew in Bulletin of American Mathematical Society : http://www.ams.org/journals/bull/1980-02-02/S0273-0979-1980-14752-7/S0273-0979-1980-14752-7.pdf -/- "Aaron Sloman is a man who is convinced that most philosophers and many other students of mind are in dire need of being convinced that there has been a revolution in that field happening right under their noses, and that they had better quickly inform themselves. The revolution is called "Artificial Intelligence" (Al)-and Sloman attempts to impart to others the "enlighten- ment" which he clearly regrets not having (...)
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  • Against direct perception.Shimon Ullman - 1980 - Behavioral and Brain Sciences 3 (3):333-81.
    Central to contemporary cognitive science is the notion that mental processes involve computations defined over internal representations. This view stands in sharp contrast to the to visual perception and cognition, whose most prominent proponent has been J.J. Gibson. In the direct theory, perception does not involve computations of any sort; it is the result of the direct pickup of available information. The publication of Gibson's recent book (Gibson 1979) offers an opportunity to examine his approach, and, more generally, to contrast (...)
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  • Intentionality and information processing: An alternative model for cognitive science.Kenneth M. Sayre - 1986 - Behavioral and Brain Sciences 9 (1):121-38.
    This article responds to two unresolved and crucial problems of cognitive science: (1) What is actually accomplished by functions of the nervous system that we ordinarily describe in the intentional idiom? and (2) What makes the information processing involved in these functions semantic? It is argued that, contrary to the assumptions of many cognitive theorists, the computational approach does not provide coherent answers to these problems, and that a more promising start would be to fall back on mathematical communication theory (...)
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  • A Delicate Balancing Act: Integrative Pluralism and the Pursuit of Unified Theories.Marcin Miłkowski - forthcoming - Foundations of Science:1-20.
    This paper examines the interplay between integrative explanatory pluralism and the quest for unified theories. We argue that when grounded in virtues associated with satisfactory explanations, integrative pluralism exhibits an inherent instability stemming from the conflict between the demand for unity and the commitment to preserving a patchwork of disparate partial explanations. A case study in cognitive science illuminates the challenges of maintaining both systematicity and depth in explanations within this framework. While this instability does not render integrative pluralism fundamentally (...)
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  • (1 other version)Forms of Luminosity: Epistemic Modality and Hyperintensionality in Mathematics.David Elohim - 2017 - Dissertation, Arché, University of St Andrews
    This book concerns the foundations of epistemic modality and hyperintensionality and their applications to the philosophy of mathematics. I examine the nature of epistemic modality, when the modal operator is interpreted as concerning both apriority and conceivability, as well as states of knowledge and belief. The book demonstrates how epistemic modality and hyperintensionality relate to the computational theory of mind; metaphysical modality and hyperintensionality; the types of mathematical modality and hyperintensionality; to the epistemic status of large cardinal axioms, undecidable propositions, (...)
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  • (1 other version)Forms of Luminosity: Epistemic Modality and Hyperintensionality in Mathematics.David Elohim - 2017
    This book concerns the foundations of epistemic modality and hyperintensionality and their applications to the philosophy of mathematics. I examine the nature of epistemic modality, when the modal operator is interpreted as concerning both apriority and conceivability, as well as states of knowledge and belief. The book demonstrates how epistemic modality and hyperintensionality relate to the computational theory of mind; metaphysical modality and hyperintensionality; the types of mathematical modality and hyperintensionality; to the epistemic status of large cardinal axioms, undecidable propositions, (...)
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  • Species differences and principles of learning: Informed generality.A. W. Logue - 1981 - Behavioral and Brain Sciences 4 (1):150-151.
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  • Semantic information: Inference rules + memory.Michael Lebowitz - 1986 - Behavioral and Brain Sciences 9 (1):147-148.
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  • (1 other version)Confirmation and the computational paradigm, or, why do you think they call it artificial intelligence?David J. Buller - 1993 - Minds and Machines 3 (2):155-81.
    The idea that human cognitive capacities are explainable by computational models is often conjoined with the idea that, while the states postulated by such models are in fact realized by brain states, there are no type-type correlations between the states postulated by computational models and brain states (a corollary of token physicalism). I argue that these ideas are not jointly tenable. I discuss the kinds of empirical evidence available to cognitive scientists for (dis)confirming computational models of cognition and argue that (...)
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  • Cognitivism about Epistemic Modality.David Elohim - manuscript
    This paper aims to vindicate the thesis that cognitive computational properties are abstract objects implemented in physical systems. I avail of the equivalence relations countenanced in Homotopy Type Theory, in order to specify an abstraction principle for epistemic intensions. The homotopic abstraction principle for epistemic intensions provides an epistemic conduit into our knowledge of intensions as abstract objects. I examine, then, how intensional functions in Epistemic Modal Algebra are deployed as core models in the philosophy of mind, Bayesian perceptual psychology, (...)
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  • Models in cognitive psychology: contrast and constraint.Herbert R. Otto - 1979 - Behavioral and Brain Sciences 2 (3):485-486.
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  • The nature of learning explanations.John Garcia - 1981 - Behavioral and Brain Sciences 4 (1):143-144.
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  • Intentionality and the explanation of behavior.John Heil - 1986 - Behavioral and Brain Sciences 9 (1):146-147.
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  • Perception, information, and computation.S. Ullman - 1980 - Behavioral and Brain Sciences 3 (3):408-415.
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  • On the nature of information in behalf of direct perception.Rebecca K. Jones & Anne D. Pick - 1980 - Behavioral and Brain Sciences 3 (3):388-389.
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  • Why argue about direct perception?J. J. Koenderink - 1980 - Behavioral and Brain Sciences 3 (3):390-391.
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  • Direct perception or mediated perception: a comparison of rival viewpoints.William Epstein - 1980 - Behavioral and Brain Sciences 3 (3):384-385.
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  • The relevance of phylogenetics to the study of behavioral diversity.Michael T. Ghiselin - 1981 - Behavioral and Brain Sciences 4 (1):144-145.
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  • Linking the biological functions and the mechanisms of learning: Uses and abuses.Patrick Bateson - 1981 - Behavioral and Brain Sciences 4 (1):142-142.
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  • Inferring the meaning of direct perception.Geoffrey E. Hinton - 1980 - Behavioral and Brain Sciences 3 (3):387-388.
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  • Semantic content: In defense of a network approach.Paul M. Churchland - 1986 - Behavioral and Brain Sciences 9 (1):139-140.
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  • The ecology of learning: The right answer to the wrong question.Barry Schwartz - 1981 - Behavioral and Brain Sciences 4 (1):159-160.
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  • Principles of learning and the ecological style of inquiry.Thomas R. Alley & Robert E. Shaw - 1981 - Behavioral and Brain Sciences 4 (1):139-141.
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  • Not an alternative model for intentionality in vision.R. Brown, D. C. Earle & S. E. G. Lea - 1986 - Behavioral and Brain Sciences 9 (1):138-139.
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  • The Computer Revolution in Philosophy.Martin Atkinson & Aaron Sloman - 1980 - Philosophical Quarterly 30 (119):178.
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  • Trends and debates in cognitive psychology.George A. Miller - 1981 - Cognition 10 (1-3):215-225.
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  • Learning theory: Behavioral artifacts or general principles?John A. Nevin - 1981 - Behavioral and Brain Sciences 4 (1):152-153.
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  • Adaptive modification of behavior: Processing information from the environment.Wolfgang M. Schleidt - 1981 - Behavioral and Brain Sciences 4 (1):158-159.
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  • Information is in the eye of the beholder.Rhea T. Eskew - 1986 - Behavioral and Brain Sciences 9 (1):144-144.
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  • On some specific models of intentional behavior.Richard M. Golden - 1986 - Behavioral and Brain Sciences 9 (1):144-145.
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  • Uncertainty about information.Ian E. Gordon - 1986 - Behavioral and Brain Sciences 9 (1):146-146.
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  • There is more to psychological meaningfulness than computation and representation.Sverker Runeson - 1980 - Behavioral and Brain Sciences 3 (3):399-400.
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  • What kind of indirect process is visual perception?Aaron Sloman - 1980 - Behavioral and Brain Sciences 3 (3):401-404.
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  • In defense of invariances and higher-order stimuli.K. von Fieandt - 1980 - Behavioral and Brain Sciences 3 (3):404-405.
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  • Visual perception is underdetermined by stimulation.John W. Gyr - 1980 - Behavioral and Brain Sciences 3 (3):386-386.
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  • (1 other version)Confirmation and the computational paradigm (or: Why do you think they call itartificial intelligence?). [REVIEW]David J. Buller - 1993 - Minds and Machines 3 (2):155-181.
    The idea that human cognitive capacities are explainable by computational models is often conjoined with the idea that, while the states postulated by such models are in fact realized by brain states, there are no type-type correlations between the states postulated by computational models and brain states (a corollary of token physicalism). I argue that these ideas are not jointly tenable. I discuss the kinds of empirical evidence available to cognitive scientists for (dis)confirming computational models of cognition and argue that (...)
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  • Discussing learning: The quandary of substance.Jack P. Hailman - 1981 - Behavioral and Brain Sciences 4 (1):146-146.
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