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  1. Varol Akman (1998). Book Review -- John Haugeland (Editor), Mind Design II: Philosophy, Psychology, and Artificial Intelligence. [REVIEW] Philosophical Explorations.
    This is a review of Mind Design II: Philosophy, Psychology, and Artificial Intelligence, edited by John Haugeland, published by MIT Press in 1997.
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  2. Varol Akman & Paul J. W. ten Hagen (1989). The Power of Physical Representations. AI Magazine 10 (3):49-65.
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  3. Murat Aydede (2000). Computation and Intentional Psychology. Dialogue 39 (2):365-379.
    The relation between computational and intentional psychology has always been a vexing issue. The worry is that if mental processes are computational, then these processes, which are defined over symbols, are sensitive solely to the non-semantic properties of symbols. If so, perhaps psychology could dispense with adverting in its laws to intentional/semantic properties of symbols. Stich, as is well-known, has made a great deal out of this tension and argued for a purely "syntactic" psychology by driving a wedge between a (...)
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  4. Murat Aydede (2000). On the Type/Token Relation of Mental Representations. Facta Philosophica 2 (1):23-50.
    According to the Computational/Representational Theory of Thought (CRTT ? Language of Thought Hypothesis, or LOTH), propositional attitudes, such as belief, desire, and the like, are triadic relations among subjects, propositions, and internal mental representations. These representations form a representational _system_ physically realized in the brain of sufficiently sophisticated cognitive organisms. Further, this system of representations has a combinatorial syntax and semantics, but the processes that operate on the representations are causally sensitive only to their syntax, not to their semantics. On (...)
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  5. Andrew R. Bailey (1994). Representations Versus Regularities: Does Computation Require Representation? Eidos 12 (1):47-58.
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  6. Tyler D. Bancroft (2013). Ethical Aspects of Computational Neuroscience. Neuroethics 6 (2):415-418.
    Recent research in computational neuroscience has demonstrated that we now possess the ability to simulate neural systems in significant detail and on a large scale. Simulations on the scale of a human brain have recently been reported. The ability to simulate entire brains (or significant portions thereof) would be a revolutionary scientific advance, with substantial benefits for brain science. However, the prospect of whole-brain simulation comes with a set of new and unique ethical questions. In the present paper, we briefly (...)
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  7. John A. Barnden & Kankanahalli Srinivas (1996). Quantification Without Variables in Connectionism. Minds and Machines 6 (2):173-201.
    Connectionist attention to variables has been too restricted in two ways. First, it has not exploited certain ways of doing without variables in the symbolic arena. One variable-avoidance method, that of logical combinators, is particularly well established there. Secondly, the attention has been largely restricted to variables in long-term rules embodied in connection weight patterns. However, short-lived bodies of information, such as sentence interpretations or inference products, may involve quantification. Therefore short-lived activation patterns may need to achieve the effect of (...)
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  8. Bert Baumgaertner (2012). Vagueness Intuitions and the Mobility of Cognitive Sortals. Minds and Machines 22 (3):213-234.
    One feature of vague predicates is that, as far as appearances go, they lack sharp application boundaries. I argue that we would not be able to locate boundaries even if vague predicates had sharp boundaries. I do so by developing an idealized cognitive model of a categorization faculty which has mobile and dynamic sortals (`classes', `concepts' or `categories') and formally prove that the degree of precision with which boundaries of such sortals can be located is inversely constrained by their flexibility. (...)
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  9. William Bechtel & Adele Abrahamsen (2010). Dynamic Mechanistic Explanation: Computational Modeling of Circadian Rhythms as an Exemplar for Cognitive Science. Studies in History and Philosophy of Science Part A 41 (3):321-333.
    Two widely accepted assumptions within cognitive science are that (1) the goal is to understand the mechanisms responsible for cognitive performances and (2) computational modeling is a major tool for understanding these mechanisms. The particular approaches to computational modeling adopted in cognitive science, moreover, have significantly affected the way in which cognitive mechanisms are understood. Unable to employ some of the more common methods for conducting research on mechanisms, cognitive scientists’ guiding ideas about mechanism have developed in conjunction with their (...)
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  10. Giuseppe Boccignone & Roberto Cordeschi (2012). Predictive Brains: Forethought and the Levels of Explanation. Frontiers in Psychology 3 (511).
    Is any unified theory of brain function possible? Following a line of thought dating back to the early cybernetics (see, e.g., Cordeschi, 2002), Clark (in press) has proposed the action-oriented Hierarchical Predictive Coding (HPC) as the account to be pursued in the effort of gaining the “Grand Unified Theory of the Mind”—or “painting the big picture,” as (Edelman 2012) put it. Such line of thought is indeed appealing, but to be effectively pursued it should be confronted with experimental findings and (...)
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  11. Keith Butler (1998). Content, Computation, and Individuation. Synthese 114 (2):277-92.
    The role of content in computational accounts of cognition is a matter of some controversy. An early prominent view held that the explanatory relevance of content consists in its supervenience on the the formal properties of computational states (see, e.g., Fodor 1980). For reasons that derive from the familiar Twin Earth thought experiments, it is usually thought that if content is to supervene on formal properties, it must be narrow; that is, it must not be the sort of content that (...)
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  12. David J. Chalmers, Robert M. French & Douglas R. Hofstadter (1992). High-Level Perception, Representation, and Analogy:A Critique of Artificial Intelligence Methodology. Journal of Experimental and Theoretical Artificial Intellige 4 (3):185 - 211.
    High-level perception--”the process of making sense of complex data at an abstract, conceptual level--”is fundamental to human cognition. Through high-level perception, chaotic environmen- tal stimuli are organized into the mental representations that are used throughout cognitive pro- cessing. Much work in traditional artificial intelligence has ignored the process of high-level perception, by starting with hand-coded representations. In this paper, we argue that this dis- missal of perceptual processes leads to distorted models of human cognition. We examine some existing artificial-intelligence models--”notably (...)
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  13. Andy Clark (1998). Twisted Tales: Causal Complexity and Cognitive Scientific Explanation. [REVIEW] Minds and Machines 8 (1):79-99.
    Recent work in biology and cognitive science depicts a variety of target phenomena as the products of a tangled web of causal influences. Such influences may include both internal and external factors as well as complex patterns of reciprocal causal interaction. Such twisted tales are sometimes seen as a threat to explanatory strategies that invoke notions such as inner programs, genes for and sometimes even internal representations. But the threat, I shall argue, is more apparent than real. Complex causal influence, (...)
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  14. Tim Crane (2003). The Mechanical Mind: A Philosophical Introduction to Minds, Machines, and Mental Representation. Routledge.
    This edition has been fully revised and updated, and includes a new chapter on consciousness and a new section on modularity. There are also guides for further reading, and a new glossary of terms such as mentalese, connectionism, and the homunculus fallacy.
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  15. Terry Dartnall (2000). Reverse Psychologism, Cognition and Content. Minds and Machines 10 (1):31-52.
    The confusion between cognitive states and the content of cognitive states that gives rise to psychologism also gives rise to reverse psychologism. Weak reverse psychologism says that we can study cognitive states by studying content – for instance, that we can study the mind by studying linguistics or logic. This attitude is endemic in cognitive science and linguistic theory. Strong reverse psychologism says that we can generate cognitive states by giving computers representations that express the content of cognitive states and (...)
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  16. David Davenport (2012). Computationalism: Still the Only Game in Town. [REVIEW] Minds and Machines 22 (3):183-190.
    Abstract Mental representations, Swiatczak (Minds Mach 21:19–32, 2011) argues, are fundamentally biochemical and their operations depend on consciousness; hence the computational theory of mind, based as it is on multiple realisability and purely syntactic operations, must be wrong. Swiatczak, however, is mistaken. Computation, properly understood, can afford descriptions/explanations of any physical process, and since Swiatczak accepts that consciousness has a physical basis, his argument against computationalism must fail. Of course, we may not have much idea how consciousness (itself a rather (...)
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  17. Eric Dietrich (2001). It Does So. [REVIEW] AI Magazine 22 (4):141-144.
    Objections to AI and computational cognitive science are myriad. Accordingly, there are many different reasons for these attacks. But all of them come down to one simple observation: humans seem a lot smarter that computers -- not just smarter as in Einstein was smarter than I, or I am smarter than a chimpanzee, but more like I am smarter than a pencil sharpener. To many, computation seems like the wrong paradigm for studying the mind. (Actually, I think there are deeper (...)
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  18. Eric Dietrich (2001). It Does So: Review of Jerry Fodor, The Mind Doesn't Work That Way. [REVIEW] AI Magazine 22 (4):121-24.
    Objections to AI and computational cognitive science are myriad. Accordingly, there are many different reasons for these attacks. But all of them come down to one simple observation: humans seem a lot smarter that computers -- not just smarter as in Einstein was smarter than I, or I am smarter than a chimpanzee, but more like I am smarter than a pencil sharpener. To many, computation seems like the wrong paradigm for studying the mind. (Actually, I think there are deeper (...)
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  19. Eric Dietrich (ed.) (1994). Thinking Computers and Virtual Persons. Academic Press.
  20. Eric Dietrich (1988). Computers, Intentionality, and the New Dualism. Computers and Philosophy Newsletter.
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  21. Gordana Dodig Crnkovic & Mark Burgin (eds.) (forthcoming). INFORMATION AND COMPUTATION. World Scientific.
    The book focuses on relations between information and computation. Information is a basic structure of the world, while computation is a process of the dynamic change of information. In order for anything to exist for an individual, the individual must get information on it, either by means of perception or by re-organization of the existing information into new patterns and networks in the brain. With the advent of World Wide Web and a prospect of semantic web, the ways of information (...)
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  22. Hubert L. Dreyfus (1979). A Framework for Misrepresenting Knowledge. In Martin Ringle (ed.), Philosophical Perspectives in Artificial Intelligence. Humanities Press.
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  23. Ricardo Restrepo Echavarria (2009). Russell's Structuralism and the Supposed Death of Computational Cognitive Science. Minds and Machines 19 (2):181-197.
    John Searle believes that computational properties are purely formal and that consequently, computational properties are not intrinsic, empirically discoverable, nor causal; and therefore, that an entity’s having certain computational properties could not be sufficient for its having certain mental properties. To make his case, Searle’s employs an argument that had been used before him by Max Newman, against Russell’s structuralism; one that Russell himself considered fatal to his own position. This paper formulates a not-so-explored version of Searle’s problem with computational (...)
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  24. Chris Eliasmith (2002). The Myth of the Turing Machine: The Failings of Functionalism and Related Theses. Journal of Experimental and Theoretical Artificial Intelligence 14 (1):1-8.
    The properties of Turing’s famous ‘universal machine’ has long sustained functionalist intuitions about the nature of cognition. Here, I show that there is a logical problem with standard functionalist arguments for multiple realizability. These arguments rely essentially on Turing’s powerful insights regarding computation. In addressing a possible reply to this criticism, I further argue that functionalism is not a useful approach for understanding what it is to have a mind. In particular, I show that the difficulties involved in distinguishing implementation (...)
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  25. Tomer Fekete & Shimon Edelman (2012). The (Lack of) Mental Life of Some Machines. In Shimon Edelman, Tomer Fekete & Neta Zach (eds.), Being in Time: Dynamical Models of Phenomenal Experience. John Benjamins.. 88--95.
    The proponents of machine consciousness predicate the mental life of a machine, if any, exclusively on its formal, organizational structure, rather than on its physical composition. Given that matter is organized on a range of levels in time and space, this generic stance must be further constrained by a principled choice of levels on which the posited structure is supposed to reside. Indeed, not only must the formal structure fit well the physical system that realizes it, but it must do (...)
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  26. James H. Fetzer (1989). Language and Mentality: Computational, Representational, and Dispositional Conceptions. Behaviorism 17:21-39.
  27. Christopher A. Fields (1994). Real Machines and Virtual Intentionality: An Experimentalist Takes on the Problem of Representational Content. In Eric Dietrich (ed.), Thinking Computers and Virtual Persons. Academic Press.
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  28. Carrie Figdor (2009). Semantic Externalism and the Mechanics of Thought. Minds and Machines 19 (1):1-24.
    I review a widely accepted argument to the conclusion that the contents of our beliefs, desires and other mental states cannot be causally efficacious in a classical computational model of the mind. I reply that this argument rests essentially on an assumption about the nature of neural structure that we have no good scientific reason to accept. I conclude that computationalism is compatible with wide semantic causal efficacy, and suggest how the computational model might be modified to accommodate this possibility.
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  29. James Franklin (2003). The Representation of Context: Ideas From Artificial Intelligence. Law, Probability and Risk 2:191-199.
    To move beyond vague platitudes about the importance of context in legal reasoning or natural language understanding, one must take account of ideas from artificial intelligence on how to represent context formally. Work on topics like prior probabilities, the theory-ladenness of observation, encyclopedic knowledge for disambiguation in language translation and pathology test diagnosis has produced a body of knowledge on how to represent context in artificial intelligence applications.
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  30. Marcello Frixione & Antonio Lieto (2013). Dealing with Concepts: From Cognitive Psychology to Knowledge Representation. Frontiers of Psychological and Behevioural Science 2 (3):96-106.
    Concept representation is still an open problem in the field of ontology engineering and, more generally, of knowledge representation. In particular, the issue of representing “non classical” concepts, i.e. concepts that cannot be defined in terms of necessary and sufficient conditions, remains unresolved. In this paper we review empirical evidence from cognitive psychology, according to which concept representation is not a unitary phenomenon. On this basis, we sketch some proposals for concept representation, taking into account suggestions from psychological research. In (...)
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  31. Marcello Frixione & Antonio Lieto (2012). Representing Concepts in Formal Ontologies: Compositionality Vs. Typicality Effects&Quot;,. Logic and Logical Philosophy 21 ( Logic, Reasoning and Rationalit):391-414.
    The problem of concept representation is relevant for many sub-fields of cognitive research, including psychology and philosophy, as well as artificial intelligence. In particular, in recent years it has received a great deal of attention within the field of knowledge representation, due to its relevance for both knowledge engineering as well as ontology-based technologies. However, the notion of a concept itself turns out to be highly disputed and problematic. In our opinion, one of the causes of this state of affairs (...)
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  32. Joseph S. Fulda (2012). Implications of a Logical Paradox for Computer-Dispensed Justice Reconsidered: Some Key Differences Between Minds and Machines. [REVIEW] Artificial Intelligence and Law 20 (3):321-333.
    We argued [Since this argument appeared in other journals, I am reprising it here, almost verbatim.] (Fulda in J Law Info Sci 2:230–232, 1991/AI & Soc 8(4):357–359, 1994) that the paradox of the preface suggests a reason why machines cannot, will not, and should not be allowed to judge criminal cases. The argument merely shows that they cannot now and will not soon or easily be so allowed. The author, in fact, now believes that when—and only when—they are ready they (...)
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  33. Joseph S. Fulda (1988). The Logic of Expert Judging Systems and the Rights of the Accused. AI and Society 2 (3):266-269.
    Deals with the problem of enthymemes in expert systems designed to model legal reasoning; suggests that interactivity is crucial.
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  34. Francisco Calvo Garzon & Angel Garcia Rodriguez (2009). Where is Cognitive Science Heading? Minds and Machines 19 (3):301-318.
    According to Ramsey (Representation reconsidered, Cambridge University Press, New York, 2007), only classical cognitive science, with the related notions of input–output and structural representations, meets the job description challenge (the challenge to show that a certain structure or process serves a representational role at the subpersonal level). By contrast, connectionism and other nonclassical models, insofar as they exploit receptor and tacit notions of representation, are not genuinely representational. As a result, Ramsey submits, cognitive science is taking a U-turn from representationalism (...)
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  35. Raymond W. Gibbs (2006). Embodiment and Cognitive Science. New York ;Cambridge University Press.
    This book explores how people's subjective, felt experiences of their bodies in action provide part of the fundamental grounding for human cognition and language. Cognition is what occurs when the body engages the physical and cultural world and must be studied in terms of the dynamical interactions between people and the environment. Human language and thought emerge from recurring patterns of embodied activity that constrain ongoing intelligent behavior. We must not assume cognition to be purely internal, symbolic, computational, and disembodied, (...)
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  36. Halil A. Guvenir & Varol Akman (1992). Problem Representation for Refinement. Minds and Machines 2 (3):267-282.
    In this paper we attempt to develop a problem representation technique which enables the decomposition of a problem into subproblems such that their solution in sequence constitutes a strategy for solving the problem. An important issue here is that the subproblems generated should be easier than the main problem. We propose to represent a set of problem states by a statement which is true for all the members of the set. A statement itself is just a set of atomic statements (...)
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  37. Stevan Harnad (2001). Minds, Machines and Turing: The Indistinguishability of Indistinguishables. Philosophical Explorations.
    Turing's celebrated 1950 paper proposes a very general methodological criterion for modelling mental function: total functional equivalence and indistinguishability. His criterion gives rise to a hierarchy of Turing Tests, from subtotal ("toy") fragments of our functions (t1), to total symbolic (pen-pal) function (T2 -- the standard Turing Test), to total external sensorimotor (robotic) function (T3), to total internal microfunction (T4), to total indistinguishability in every empirically discernible respect (T5). This is a "reverse-engineering" hierarchy of (decreasing) empirical underdetermination of the theory (...)
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  38. Gary Hatfield (1989). Computation, Representation and Content in Noncognitive Theories of Perception. In Stuart Silvers (ed.), ReRepresentation. Kluwer.
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  39. J. Haugel (ed.) (1981). Mind Design. MIT Press.
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  40. John Haugeland (1981). Semantic Engines: An Introduction to Mind Design. In J. Haugel (ed.), Mind Design. MIT Press.
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  41. Amir Horowitz (2007). Computation, External Factors, and Cognitive Explanations. Philosophical Psychology 20 (1):65-80.
    Computational properties, it is standardly assumed, are to be sharply distinguished from semantic properties. Specifically, while it is standardly assumed that the semantic properties of a cognitive system are externally or non-individualistically individuated, computational properties are supposed to be individualistic and internal. Yet some philosophers (e.g., Tyler Burge) argue that content impacts computation, and further, that environmental factors impact computation. Oron Shagrir has recently argued for these theses in a novel way, and gave them novel interpretations. In this paper I (...)
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  42. Alistair Isaac & Jakub Szymanik (2010). Logic in Cognitive Science: Bridging the Gap Between Symbolic and Connectionist Paradigms. Journal of the Indian Council of Philosophical Research (2):279-309.
    This paper surveys applications of logical methods in the cognitive sciences. Special attention is paid to non-monotonic logics and complexity theory. We argue that these particular tools have been useful in clarifying the debate between symbolic and connectionist models of cognition.
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  43. David Michael Kaplan (2011). Explanation and Description in Computational Neuroscience. Synthese 183 (3):339-373.
    The central aim of this paper is to shed light on the nature of explanation in computational neuroscience. I argue that computational models in this domain possess explanatory force to the extent that they describe the mechanisms responsible for producing a given phenomenon—paralleling how other mechanistic models explain. Conceiving computational explanation as a species of mechanistic explanation affords an important distinction between computational models that play genuine explanatory roles and those that merely provide accurate descriptions or predictions of phenomena. It (...)
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  44. J. R. Kazez (1994). Computationalism and the Causal Role of Content. Philosophical Studies 75 (3):231-60.
  45. John-Michael M. Kuczynski (2006). Formal Operations and Simulated Thought. Philosophical Explorations 9 (2):221-234.
    A series of representations must be semantics-driven if the members of that series are to combine into a single thought. Where semantics is not operative, there is at most a series of disjoint representations that add up to nothing true or false, and therefore do not constitute a thought at all. There is necessarily a gulf between simulating thought, on the one hand, and actually thinking, on the other. A related point is that a popular doctrine - the so-called 'computational (...)
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  46. R. P. Loui & Jeff Norman (1995). Rationales and Argument Moves. Artificial Intelligence and Law 3 (3):159-189.
    We discuss five kinds of representations of rationales and provide a formal account of how they can alter disputation. The formal model of disputation is derived from recent work in argument. The five kinds of rationales are compilation rationales, which can be represented without assuming domain-knowledge (such as utilities) beyond that normally required for argument. The principal thesis is that such rationales can be analyzed in a framework of argument not too different from what AI already has. The result is (...)
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  47. Stella F. Lourenco & Susan C. Levine (2008). Early Numerical Representations and the Natural Numbers: Is There Really a Complete Disconnect? Behavioral and Brain Sciences 31 (6):660-660.
    The proposal of Rips et al. is motivated by discontinuity and input claims. The discontinuity claim is that no continuity exists between early (nonverbal) numerical representations and natural number. The input claim is that particular experiences (e.g., cardinality-related talk and object-based activities) do not aid in natural number construction. We discuss reasons to doubt both claims in their strongest forms.
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  48. Laureano Luna & Christopher Small (2009). Intentionality and Computationalism. A Diagonal Argument. Mind and Matter 7 (1):81-90.
    Computationalism is the claim that all possible thoughts are computations, i.e. executions of algorithms. The aim of the paper is to show that if intentionality is semantically clear, in a way defined in the paper, then computationalism must be false. Using a convenient version of the phenomenological relation of intentionality and a diagonalization device inspired by Thomson's theorem of 1962, we show there exists a thought that canno be a computation.
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  49. Robert W. Lurz (2012). Origins of Objectivity. Philosophical Psychology 25 (5):775-781.
    Philosophical Psychology, Volume 0, Issue 0, Page 1-7, Ahead of Print.
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  50. Craig R. M. McKenzie (2009). Bayes Plus Environment. Behavioral and Brain Sciences 32 (1):93-94.
    Oaksford & Chater's (O&C's) account of deductive reasoning is parsimonious at a local level (because a rational model is used to explain a wide range of behavior) and at a global level (because their Bayesian approach connects to other areas of research). Their emphasis on environmental structure is especially important, and the power of their approach is seen at both the computational and algorithmic levels.
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