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  1. Hervé Abdi, Dominique Valentin & Betty G. Edelman (1998). Eigenfeatures as Intermediate-Level Representations: The Case for PCA Models. Behavioral and Brain Sciences 21 (1):17-18.
    Eigenfeatures are created by the principal component approach (PCA) used on objects described by a low-level code (i.e., pixels, Gabor jets). We suggest that eigenfeatures act like the flexible features described by Schyns et al. They are particularly suited for face processing and give rise to class-specific effects such as the other-race effect. The PCA approach can be modified to accommodate top-down constraints.
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  2. Robert P. Abelson (1980). Searle's Argument is Just a Set of Chinese Symbols. Behavioral and Brain Sciences 3 (3):424.
  3. Ana Margarida Abrantes (2009). Cognition and Culture. Semiotics:480-486.
  4. Juan José Acero (1998). Non-Conceptual Content, Subject-Centered Information and the Naturalistic Demand. Philosophical Issues 9:359-367.
  5. Joana Acha & Manuel Perea (2008). The Effect of Neighborhood Frequency in Reading: Evidence with Transposed-Letter Neighbors. Cognition 108 (1):290-300.
  6. Fred Adams & Ken Aizawa (1997). Rock Beats Scissors: Historicalism Fights Back. Analysis 57 (4):273 - 281.
  7. Edward H. Adelson (1983). What is Iconic Storage Good For? Behavioral and Brain Sciences 6 (1):11.
  8. George Ainslie (2007). Game Theory Can Build Higher Mental Processes From Lower Ones. Behavioral and Brain Sciences 30 (1):16-18.
    The question of reductionism is an obstacle to unification. Many behavioral scientists who study the more complex or higher mental functions avoid regarding them as selected by motivation. Game-theoretic models in which complex processes grow from the strategic interaction of elementary reward-seeking processes can overcome the mechanical feel of earlier reward-based models. Three examples are briefly described. (Published Online April 27 2007).
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  9. Kenneth Aizawa (2013). Introduction to “The Material Bases of Cognition”. Minds and Machines 23 (3):277-286.
  10. Kenneth Aizawa & Frederick R. Adams (2005). Defending Non-Derived Content. Philosophical Psychology 18 (6):661-669.
    In ‘‘The Myth of Original Intentionality,’’ Daniel Dennett appears to want to argue for four claims involving the familiar distinction between original (or underived) and derived intentionality.
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  11. D. Alais (1999). Spatial Structure From Temporal Change. Trends in Cognitive Sciences 3 (8):293.
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  12. Marc K. Albert (2003). The Whole of Recognition. Trends in Cognitive Sciences 7 (12):522-524.
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  13. Liliana Albertazzi (2009). Images, Spaces, Representations. Axiomathes 19 (1):103-111.
    The contribution deals with some key problems of cognitive science, whose plurality transcends the boundaries of the disciplines drawn by classical epistemology. In particular, it addresses the issues of mental images, spaces of representation, and the architecture of cognitive processes in vision theory. The thesis presented is that a proper treatment of vision within psychophysics entails an analysis of a series of interconnected spaces, objects and methodologies, from psychophysics to the many virtual realities of representation.
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  14. Garrett E. Alexander (1992). For Effective Sensorimotor Processing Must There Be Explicit Representations and Reconciliation of Differing Frames of Reference? Behavioral and Brain Sciences 15 (2):321-322.
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  15. Joshua Alexander, Ronald Mallon & Jonathan Weinberg (2010). Competence: What's In? What's Out? Who Knows? Behavioral and Brain Sciences 33 (4):329-330.
    Knobe's argument rests on a way of distinguishing performance errors from the competencies that delimit our cognitive architecture. We argue that other sorts of evidence than those that he appeals to are needed to illuminate the boundaries of our folk capacities in ways that would support his conclusions.
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  16. Jedediah Wp Allen & Mark H. Bickhard (2011). You Can't Get There From Here: Foundationalism and Development. Behavioral and Brain Sciences 34 (3):124-125.
    The thesis of our commentary is that the framework used to address what are taken by Carey to be the open issues is highly problematic. The presumed necessity of an innate stock of representational primitives fails to account for the emergence of representation out of a nonrepresentational base. This failure manifests itself in problematic ways throughout Carey's book.
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  17. Fritz Allhoff (ed.) (2010). Philosophies of the Sciences. Wiley-Blackwell.
    The essays are written by leading scholars in a highly accessible style for the student audience Presents and discusses central debates in the field, making it ...
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  18. D. A. Allport (1983). Language and Cognition. In Roy Harris (ed.), Approaches to Language. Pergamon 61--94.
  19. Hedy Amiri & Chad J. Marsolek (2002). Depicting Second-Order Isomorphism and “Depictive” Representations. Behavioral and Brain Sciences 25 (2):182-183.
    According to Pylyshyn, depictive representations can be explanatory only if a certain kind of first-order isomorphism exists between the mental representations and real-world displays. What about a system with second-order isomorphism (similarities between different mental representations corresponding with similarities between different real-world displays)? Such a system may help to address whether “depictive” representations contribute to the visual nature of imagery.
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  20. Daniel J. Amit (1995). The Hebbian Paradigm Reintegrated: Local Reverberations as Internal Representations. Behavioral and Brain Sciences 18 (4):617.
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  21. Dr Michael L. Anderson (2003). Representations, Symbols and Embodiment. Philosophical Explorations.
    Response to "Embodied artificial intelligence", a commentary by Ron Chrisley.
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  22. James A. Anderson (2003). Arithmetic on a Parallel Computer: Perception Versus Logic. [REVIEW] Brain and Mind 4 (2):169-188.
    This article discusses the properties of a controllable, flexible, hybrid parallel computing architecture that potentially merges pattern recognition and arithmetic. Humans perform integer arithmetic in a fundamentally different way than logic-based computers. Even though the human approach to arithmetic is both slow and inaccurate it can have substantial advantages when useful approximations ( intuition ) are more valuable than high precision. Such a computational strategy may be particularly useful when computers based on nanocomponents become feasible because it offers a way (...)
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  23. James A. Anderson (1991). Review: On What Building a Martian Three-Wheeled Iguana Tells Us About Complex Minds. [REVIEW] Behavior and Philosophy 19 (2):91 - 102.
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  24. John R. Anderson (1983). Representation Without Process? Behavioral and Brain Sciences 6 (1):137.
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  25. Michael L. Anderson (2003). Embodied Cognition: A Field Guide. Artificial Intelligence 149 (1):91-130.
    The nature of cognition is being re-considered. Instead of emphasizing formal operations on abstract symbols, the new approach foregrounds the fact that cognition is, rather, a situated activity, and suggests that thinking beings ought therefore be considered first and foremost as acting beings. The essay reviews recent work in Embodied Cognition, provides a concise guide to its principles, attitudes and goals, and identifies the physical grounding project as its central research focus.
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  26. Michael L. Anderson & Tim Oates (2003). Prelinguistic Agents Will Form Only Egocentric Representations. Behavioral and Brain Sciences 26 (3):284-285.
    The representations formed by the ventral and dorsal streams of a prelinguistic agent will tend to be too qualitatively similar to support the distinct roles required by PREDICATE(x) structure. We suggest that the attachment of qualities to objects is not a product of the combination of these separate processing streams, but is instead a part of the processing required in each. In addition, we suggest that the formation of objective predicates is inextricably bound up with the emergence of language itself, (...)
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  27. Norman H. Anderson (1996). Cognitive Algebra Versus Representativeness Heuristic. Behavioral and Brain Sciences 19 (1):17.
  28. Norman H. Anderson (1989). Speech Perception as Information Integration. Behavioral and Brain Sciences 12 (4):755.
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  29. David R. Andresen & Chad J. Marsolek (1998). Chorus of K Prototypes or Discord of Contradictory Representations? Behavioral and Brain Sciences 21 (4):467-468.
    The human visual system is capable of learning both abstract and specific mappings to underlie shape recognition. How could dissimilar shapes be mapped to the same location in visual representation space, yet similar shapes be mapped to different locations? Without fundamental changes, Chorus, like other single-system models, could not accomplish both mappings in a manner that accounts for recent evidence.
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  30. Avery D. Andrews (1993). Mental Models and Tableau Logic. Behavioral and Brain Sciences 16 (2):334.
  31. Daniel Ansari (2009). Are Non-Abstract Brain Representations of Number Developmentally Plausible? Behavioral and Brain Sciences 32 (3-4):329-330.
    The theory put forward by Cohen Kadosh & Walsh (CK&W) proposing that semantic representations of numerical magnitude in the parietal cortex are format-specific, does not specify how these representations might be constructed over the course of learning and development. The developmental predictions of the non-abstract theory are discussed and the need for a developmental perspective on the abstract versus non-abstract question highlighted.
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  32. Radical Answers (1991). Of Mental Representations. In Terence E. Horgan & John L. Tienson (eds.), Connectionism and the Philosophy of Mind. Kluwer 9--355.
  33. Louise M. Antony (2002). How to Play the Flute: A Commentary on Dreyfus's “Intelligence Without Representation”. [REVIEW] Phenomenology and the Cognitive Sciences 1 (4):395-401.
  34. K. Apel, Julie A. Wolter & J. J. Masterson (2011). Mental Graphemic Representations (MGRs). In Norbert M. Seel (ed.), Encyclopedia of the Sciences of Learning. Springer Verlag
  35. Marc Artiga (2014). Prinz's Naturalistic Theory of Intentional Content. Critica 46 (136):69-86.
    This paper addresses Prinz's naturalistic theory of conceptual content, which he has defended in several works (Prinz, 2000; 2002; 2006). More precisely, I present in detail and critically assess his account of referential content, which he distinguishes from nominal or cognitive content. The paper argues that Prinz's theory faces four important difficulties, which might have significant consequences for his overall empiricist project.
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  36. Luis M. Augusto (2014). Unconscious Representations 2: Towards an Integrated Cognitive Architecture. [REVIEW] Axiomathes 24 (1):19-43.
    The representational nature of human cognition and thought in general has been a source of controversies. This is particularly so in the context of studies of unconscious cognition, in which representations tend to be ontologically and structurally segregated with regard to their conscious status. However, it appears evolutionarily and developmentally unwarranted to posit such segregations, as,otherwise, artifact structures and ontologies must be concocted to explain them from the viewpoint of the human cognitive architecture. Here, from a by-and-large Classical cognitivist viewpoint, (...)
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  37. Luis M. Augusto (2013). Unconscious Representations 1: Belying the Traditional Model of Human Cognition. [REVIEW] Axiomathes 23 (4):1-19.
    The traditional model of human cognition (TMHC) postulates an ontological and/or structural gap between conscious and unconscious mental representations. By and large, it sees higher-level mental processes as commonly conceptual or symbolic in nature and therefore conscious, whereas unconscious, lower-level representations are conceived as non-conceptual or sub-symbolic. However, experimental evidence belies this model, suggesting that higher-level mental processes can be, and often are, carried out in a wholly unconscious way and/or without conceptual representations, and that these can be processed unconsciously. (...)
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  38. Theodore Bach (2012). Analogical Cognition: Applications in Epistemology and the Philosophy of Mind and Language. Philosophy Compass 7 (5):348-360.
    Analogical cognition refers to the ability to detect, process, and learn from relational similarities. The study of analogical and similarity cognition is widely considered one of the ‘success stories’ of cognitive science, exhibiting convergence across many disciplines on foundational questions. Given the centrality of analogy to mind and knowledge, it would benefit philosophers investigating topics in epistemology and the philosophies of mind and language to become familiar with empirical models of analogical cognition. The goal of this essay is to describe (...)
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  39. Lynne Rudder Baker (2002). Comments on Hubert L. Dreyfus “Intelligence Without Representation”. Phenomenology and the Cognitive Sciences 1 (4):411-412.
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  40. Katalin Balog (2009). Jerry Fodor on Non-Conceptual Content. Synthese 167 (3):311 - 320.
    Proponents of non-conceptual content have recruited it for various philosophical jobs. Some epistemologists have suggested that it may play the role of “the given” that Sellars is supposed to have exorcised from philosophy. Some philosophers of mind (e.g., Dretske) have suggested that it plays an important role in the project of naturalizing semantics as a kind of halfway between merely information bearing and possessing conceptual content. Here I will focus on a recent proposal by Jerry Fodor. In a recent paper (...)
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  41. Stephen Barker (2015). Expressivism About Reference and Quantification Over the Non-Existent Without Meinongian Metaphysics. Erkenntnis 80 (S2):215-234.
    Can we believe that there are non-existent entities without commitment to the Meinongian metaphysics? This paper argues we can. What leads us from quantification over non-existent beings to Meinongianism is a general metaphysical assumption about reality at large, and not merely quantification over the non-existent. Broadly speaking, the assumption is that every being we talk about must have a real definition. It’s this assumption that drives us to enquire into the nature of beings like Pegasus, and what our relationship as (...)
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  42. L. Barsalou, W. Yeh, B. Luka, K. Olseth, K. Mix & L. Wu (eds.) (1993). Chicago Linguistic Society 29: Papers From the Parasession on Conceptual Representations. University of Chicago.
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  43. William Bechtel (2001). Representations: From Neural Systems to Cognitive Systems. In William P. Bechtel, Pete Mandik, Jennifer Mundale & Robert S. Stufflebeam (eds.), Philosophy and the Neurosciences: A Reader. Blackwell
  44. William P. Bechtel (1998). Representations and Cognitive Explanations: Assessing the Dynamicist Challenge in Cognitive Science. Cognitive Science 22 (3):295-317.
    Advocates of dynamical systems theory (DST) sometimes employ revolutionary rhetoric. In an attempt to clarify how DST models differ from others in cognitive science, I focus on two issues raised by DST: the role for representations in mental models and the conception of explanation invoked. Two features of representations are their role in standing-in for features external to the system and their format. DST advocates sometimes claim to have repudiated the need for stand-ins in DST models, but I argue that (...)
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  45. William Bechtel & Mitchell Herschbach (2010). Philosophy of the Cognitive Sciences. In Fritz Allhoff (ed.), Philosophies of the Sciences. Wiley-Blackwell 239--261.
    Cognitive science is an interdisciplinary research endeavor focusing on human cognitive phenomena such as memory, language use, and reasoning. It emerged in the second half of the 20th century and is charting new directions at the beginning of the 21st century. This chapter begins by identifying the disciplines that contribute to cognitive science and reviewing the history of the interdisciplinary engagements that characterize it. The second section examines the role that mechanistic explanation plays in cognitive science, (...)
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  46. Jacob Beck, Can Bootstrapping Explain Concept Learning?
    Susan Carey’s account of bootstrapping aims to explain how important new concepts are learned. After arguing that Carey’s own formulations of bootstrapping fail in this aim, I critically evaluate three reformulations of bootstrapping that may have a better chance at success.
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  47. Jacob Beck (2014). Analogue Magnitude Representations: A Philosophical Introduction. British Journal for the Philosophy of Science (4):axu014.
    Empirical discussions of mental representation appeal to a wide variety of representational kinds. Some of these kinds, such as the sentential representations underlying language use and the pictorial representations of visual imagery, are thoroughly familiar to philosophers. Others have received almost no philosophical attention at all. Included in this latter category are analogue magnitude representations, which enable a wide range of organisms to primitively represent spatial, temporal, numerical, and related magnitudes. This article aims to introduce analogue magnitude representations to a (...)
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  48. Jacob Beck (2013). Why We Can't Say What Animals Think. Philosophical Psychology 26 (4):520–546.
    Realists about animal cognition confront a puzzle. If animals have real, contentful cognitive states, why can’t anyone say precisely what the contents of those states are? I consider several possible resolutions to this puzzle that are open to realists, and argue that the best of these is likely to appeal to differences in the format of animal cognition and human language.
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  49. Samuel Bellini-Leite (2013). The Embodied Embedded Character of System 1 Processing. Mens Sana Monographs 11 (1):239-252.
    In the last thirty years, a relatively large group of cognitive scientists have begun characterising the mind in terms of two distinct, relatively autonomous systems. To account for paradoxes in empirical results of studies mainly on reasoning, Dual Process Theories were developed. Such Dual Process Theories generally agree that System 1 is rapid, automatic, parallel, and heuristic-based and System 2 is slow, capacity-demanding, sequential, and related to consciousness. While System 2 can still be decently understood from a traditional cognitivist approach, (...)
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  50. Mark H. Bickhard (1993). Representational Content in Humans and Machines. Journal of Experimental and Theoretical Artificial Intelligence 5:285-33.
    This article focuses on the problem of representational content. Accounting for representational content is the central issue in contemporary naturalism: it is the major remaining task facing a naturalistic conception of the world. Representational content is also the central barrier to contemporary cognitive science and artificial intelligence: it is not possible to understand representation in animals nor to construct machines with genuine representation given current (lack of) understanding of what representation is. An elaborated critique is offered to current approaches to (...)
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1 — 50 / 331