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Search results for 'Computational Theory' (try it on Scholar)

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  1. Bartlomiej Swiatczak (2011). Conscious Representations: An Intractable Problem for the Computational Theory of Mind. [REVIEW] Minds and Machines 21 (1):19-32.score: 180.0
    Advocates of the computational theory of mind claim that the mind is a computer whose operations can be implemented by various computational systems. According to these philosophers, the mind is multiply realisable because—as they claim—thinking involves the manipulation of syntactically structured mental representations. Since syntactically structured representations can be made of different kinds of material while performing the same calculation, mental processes can also be implemented by different kinds of material. From this perspective, consciousness plays a minor (...)
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  2. Jon Cogburn & Jason Megill (2010). Are Turing Machines Platonists? Inferentialism and the Computational Theory of Mind. Minds and Machines 20 (3):423-439.score: 180.0
    We first discuss Michael Dummett’s philosophy of mathematics and Robert Brandom’s philosophy of language to demonstrate that inferentialism entails the falsity of Church’s Thesis and, as a consequence, the Computational Theory of Mind. This amounts to an entirely novel critique of mechanism in the philosophy of mind, one we show to have tremendous advantages over the traditional Lucas-Penrose argument.
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  3. Gualtiero Piccinini & Sonya Bahar (2013). Neural Computation and the Computational Theory of Cognition. Cognitive Science 37 (3):453-488.score: 176.0
    We begin by distinguishing computationalism from a number of other theses that are sometimes conflated with it. We also distinguish between several important kinds of computation: computation in a generic sense, digital computation, and analog computation. Then, we defend a weak version of computationalism—neural processes are computations in the generic sense. After that, we reject on empirical grounds the common assimilation of neural computation to either analog or digital computation, concluding that neural computation is sui generis. Analog computation requires continuous (...)
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  4. Martin V. Butz Oliver Herbort (2012). Too Good to Be True? Ideomotor Theory From a Computational Perspective. Frontiers in Psychology 3.score: 144.0
    In recent years, Ideomotor Theory has regained widespread attention and sparked the development of a number of theories on goal-directed behavior and learning. However, there are two issues with previous studies’ use of Ideomotor Theory. Although Ideomotor Theory is seen as very general, it is often studied in settings that are considerably more simplistic than most natural situations. Moreover, Ideomotor Theory’s claim that effect anticipations directly trigger actions and that action-effect learning is based on the formation (...)
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  5. Louis C. Charland (1995). Feeling and Representing: Computational Theory and the Modularity of Affect. Synthese 105 (3):273-301.score: 132.0
    In this paper I review some leading developments in the empirical theory of affect. I argue that (1) affect is a distinct perceptual representation governed system, and (2) that there are significant modular factors in affect. The paper concludes with the observation thatfeeler (affective perceptual system) may be a natural kind within cognitive science. The main purpose of the paper is to explore some hitherto unappreciated connections between the theory of affect and the computational theory of (...)
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  6. Gualtiero Piccinini (2004). The First Computational Theory of Mind and Brain: A Close Look at McCulloch and Pitts' Logical Calculus of Ideas Immanent in Nervous Activity. Synthese 141 (2):175-215.score: 132.0
    Despite its significance in neuroscience and computation, McCulloch and Pitts's celebrated 1943 paper has received little historical and philosophical attention. In 1943 there already existed a lively community of biophysicists doing mathematical work on neural networks. What was novel in McCulloch and Pitts's paper was their use of logic and computation to understand neural, and thus mental, activity. McCulloch and Pitts's contributions included (i) a formalism whose refinement and generalization led to the notion of finite automata (an important formalism in (...)
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  7. Jeffrey Hershfield (2005). Is There Life After the Death of the Computational Theory of Mind? Minds and Machines 15 (2):183-194.score: 124.0
    In this paper, I explore the implications of Fodor’s attacks on the Computational Theory of Mind (CTM), which get their most recent airing in The Mind Doesn’t Work That Way. I argue that if Fodor is right that the CTM founders on the global nature of abductive inference, then several of the philosophical views about the mind that he has championed over the years founder as well. I focus on Fodor’s accounts of mental causation, psychological explanation, and intentionality.
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  8. Steven Horst (1999). Symbols and Computation: A Critique of the Computational Theory of Mind. Minds and Machines 9 (3):347-381.score: 124.0
    Over the past several decades, the philosophical community has witnessed the emergence of an important new paradigm for understanding the mind.1 The paradigm is that of machine computation, and its influence has been felt not only in philosophy, but also in all of the empirical disciplines devoted to the study of cognition. Of the several strategies for applying the resources provided by computer and cognitive science to the philosophy of mind, the one that has gained the most attention from philosophers (...)
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  9. Laura Sizer (2000). Towards a Computational Theory of Mood. British Journal for the Philosophy of Science 51 (4):743-770.score: 124.0
    Moods have global and profound effects on our thoughts, motivations and behavior. To understand human behavior and cognition fully, we must understand moods. In this paper I critically examine and reject the methodology of conventional ?cognitive theories? of affect. I lay the foundations of a new theory of moods that identifies them with processes of our cognitive functional architecture. Moods differ fundamentally from some of our other affective states and hence require distinct explanatory tools. The computational theory (...)
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  10. Marcin Milkowski, Computational Theory of Mind. Internet Encyclopedia of Philosophy.score: 124.0
    The Computational Theory of Mind The Computational Theory of Mind (CTM) claims that the mind is a computer, so the theory is also known as computationalism. It is generally assumed that CTM is the main working hypothesis of cognitive science. CTM is often understood as a specific variant of the Representational Theory of Mind (RTM), […].
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  11. William J. Rapaport & Michael W. Kibby (2002). Contextual Vocabulary Acquisition: A Computational Theory and Educational Curriculum. In Nagib Callaos, Ana Breda & Ma Yolanda Fernandez J. (eds.), Proceedings of the 6th World Multiconference on Systemics, Cybernetics and Informatics. International Institute of Informatics and Systemics.score: 120.0
    We discuss a research project that develops and applies algorithms for computational contextual vocabulary acquisition (CVA): learning the meaning of unknown words from context. We try to unify a disparate literature on the topic of CVA from psychology, first- and secondlanguage acquisition, and reading science, in order to help develop these algorithms: We use the knowledge gained from the computational CVA system to build an educational curriculum for enhancing students’ abilities to use CVA strategies in their reading of (...)
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  12. John-Michael M. Kuczynski (2006). Two Concepts of "Form" and the so-Called Computational Theory of Mind. Philosophical Psychology 19 (6):795-821.score: 120.0
    According to the computational theory of mind (CTM), to think is to compute. But what is meant by the word 'compute'? The generally given answer is this: Every case of computing is a case of manipulating symbols, but not vice versa - a manipulation of symbols must be driven exclusively by the formal properties of those symbols if it is qualify as a computation. In this paper, I will present the following argument. Words like 'form' and 'formal' are (...)
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  13. Steven Horst, The Computational Theory of Mind. Stanford Encyclopedia of Philosophy.score: 120.0
    Over the past thirty years, it is been common to hear the mind likened to a digital computer. This essay is concerned with a particular philosophical view that holds that the mind literally is a digital computer (in a specific sense of “computer” to be developed), and that thought literally is a kind of computation. This view—which will be called the “Computational Theory of Mind” (CTM)—is thus to be distinguished from other and broader attempts to connect the mind (...)
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  14. Kirk Ludwig & Susan Schneider (2008). Fodor's Challenge to the Classical Computational Theory of Mind. Mind and Language 23 (1):123–143.score: 120.0
    In The Mind Doesn’t Work that Way, Jerry Fodor argues that mental representations have context sensitive features relevant to cognition, and that, therefore, the Classical Computational Theory of Mind (CTM) is mistaken. We call this the Globality Argument. This is an in principle argument against CTM. We argue that it is self-defeating. We consider an alternative argument constructed from materials in the discussion, which avoids the pitfalls of the official argument. We argue that it is also unsound and (...)
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  15. John-Michael Kuczynski (2007). Conceptual Atomism and the Computational Theory of Mind: A Defense of Content-Internalism and Semantic Externalism. John Benjamins & Co.score: 120.0
    Contemporary philosophy and theoretical psychology are dominated by an acceptance of content-externalism: the view that the contents of one's mental states are constitutively, as opposed to causally, dependent on facts about the external world. In the present work, it is shown that content-externalism involves a failure to distinguish between semantics and pre-semantics---between, on the one hand, the literal meanings of expressions and, on the other hand, the information that one must exploit in order to ascertain their literal meanings. It is (...)
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  16. John Mikhail (2008). Moral Cognition and Computational Theory. In Walter Sinnott-Armstrong (ed.), Moral Psychology Volume 3. MIT Press.score: 120.0
    In this comment on Joshua Greene's essay, The Secret Joke of Kant's Soul, I argue that a notable weakness of Greene's approach to moral psychology is its neglect of computational theory. A central problem moral cognition must solve is to recognize (i.e., compute representations of) the deontic status of human acts and omissions. How do people actually do this? What is the theory which explains their practice?
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  17. Iep Author, Computational Theory of Mind.score: 120.0
    The Computational Theory of Mind The Computational Theory of Mind (CTM) claims that the mind is a computer, so the theory is also known as computationalism. It is generally assumed that CTM is the main working hypothesis of cognitive science. CTM is often understood as a specific variant of the Representational Theory of Mind (RTM), […].
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  18. Tomer Fekete & Shimon Edelman (2011). Towards a Computational Theory of Experience. Consciousness and Cognition 20 (3):807-827.score: 120.0
    A standing challenge for the science of mind is to account for the datum that every mind faces in the most immediate – that is, unmediated – fashion: its phenomenal experience. The complementary tasks of explaining what it means for a system to give rise to experience and what constitutes the content of experience (qualia) in computational terms are particularly challenging, given the multiple realizability of computation. In this paper, we identify a set of conditions that a computational (...)
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  19. Rainer Reisenzein (2009). Emotional Experience in the Computational Belief-Desire Theory of Emotion. Emotion Review 1 (3):214-222.score: 120.0
    Based on the belief that computational modeling (thinking in terms of representation and computations) can help to clarify controversial issues in emotion theory, this article examines emotional experience from the perspective of the Computational Belief–Desire Theory of Emotion (CBDTE), a computational explication of the belief–desire theory of emotion. It is argued that CBDTE provides plausible answers to central explanatory challenges posed by emotional experience, including: the phenomenal quality,intensity and object-directedness of emotional experience, the function (...)
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  20. K. McClelland (forthcoming). Cycles of Conflict: A Computational Modeling Alternative to Collins's Theory of Conflict Escalation. Sociological Theory.score: 114.0
    In a new theory of conflict escalation, Randall Collins (2012) engages critical issues of violent conflict and presents a compellingly plausible theoretical description based on his extensive empirical research. He also sets a new challenge for sociology: explaining the time dynamics of social interaction. However, despite heavy reliance on the quantitative concept of positive feedback loops in his theory, Collins presents no mathematical specification of the dynamic relationships among his variables. This article seeks to fill that gap by (...)
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  21. Susan Schneider, The Central System as a Computational Engine.score: 108.0
    The Language of Thought program has a suicidal edge. Jerry Fodor, of all people, has argued that although LOT will likely succeed in explaining modular processes, it will fail to explain the central system, a subsystem in the brain in which information from the different sense modalities is integrated, conscious deliberation occurs, and behavior is planned. A fundamental characteristic of the central system is that it is “informationally unencapsulated” -- its operations can draw from information from any cognitive domain. The (...)
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  22. Jonathan Waskan (2011). A Vehicular Theory of Corporeal Qualia (a Gift to Computationalists). Philosophical Studies 152 (1):103 - 125.score: 108.0
    I have argued elsewhere that non-sentential representations that are the close kin of scale models can be, and often are, realized by computational processes. I will attempt here to weaken any resistance to this claim that happens to issue from those who favor an across-the-board computational theory of cognitive activity. I will argue that embracing the idea that certain computers harbor nonsentential models gives proponents of the computational theory of cognition the means to resolve the (...)
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  23. Michael Rescorla (2014). A Theory of Computational Implementation. Synthese 191 (6):1277-1307.score: 108.0
    I articulate and defend a new theory of what it is for a physical system to implement an abstract computational model. According to my descriptivist theory, a physical system implements a computational model just in case the model accurately describes the system. Specifically, the system must reliably transit between computational states in accord with mechanical instructions encoded by the model. I contrast my theory with an influential approach to computational implementation espoused by Chalmers, (...)
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  24. Janyce M. Wiebe & William J. Rapaport (1988). A Computational Theory of Perspective and Reference in Narrative. In Proceedings of the 26th Annual Meeting of the Association for Computational Linguistics.score: 108.0
    Narrative passages told from a character's perspective convey the character's thoughts and perceptions. We present a discourse process that recognizes characters' thoughts and perceptions in third-person narrative. An effect of perspective on reference in narrative is addressed: References in passages told from the perspective of a character reflect the character's beliefs. An algorithm that uses the results of our discourse process to understand references with respect to an appropriate set of beliefs is presented.
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  25. Gualtiero Piccinini (2006). Computational Explanation in Neuroscience. Synthese 153 (3):343-353.score: 104.0
    According to some philosophers, computational explanation is proprietary
    to psychology—it does not belong in neuroscience. But neuroscientists routinely offer computational explanations of cognitive phenomena. In fact, computational explanation was initially imported from computability theory into the science of mind by neuroscientists, who justified this move on neurophysiological grounds. Establishing the legitimacy and importance of computational explanation in neuroscience is one thing; shedding light on it is another. I raise some philosophical questions pertaining to computational explanation (...)
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  26. Louis C. Charland (1995). Emotion as a Natural Kind: Towards a Computational Foundation for Emotion Theory. Philosophical Psychology 8 (1):59-84.score: 102.0
    In this paper I link two hitherto disconnected sets of results in the philosophy of emotions and explore their implications for the computational theory of mind. The argument of the paper is that, for just the same reasons that some computationalists have thought that cognition may be a natural kind, so the same can plausibly be argued of emotion. The core of the argument is that emotions are a representation-governed phenomenon and that the explanation of how they figure (...)
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  27. Jason L. Megill (2004). Are We Paraconsistent? On the Lucas-Penrose Argument and the Computational Theory of Mind. Auslegung 27 (1):23-30.score: 102.0
     
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  28. P. Morton (1993). Supervenience and Computational Explanation in Vision Theory. Philosophy of Science 60 (1):86-99.score: 98.0
    According to Marr's theory of vision, computational processes of early vision rely for their success on certain "natural constraints" in the physical environment. I examine the implications of this feature of Marr's theory for the question whether psychological states supervene on neural states. It is reasonable to hold that Marr's theory is nonindividualistic in that, given the role of natural constraints, distinct computational theories of the same neural processes may be justified in different environments. But (...)
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  29. P. S. Kitcher (1988). Marr's Computational Theory of Vision. Philosophy of Science 55 (March):1-24.score: 96.0
    David Marr's theory of vision has been widely cited by philosophers and psychologists. I have three projects in this paper. First, I try to offer a perspicuous characterization of Marr's theory. Next, I consider the implications of Marr's work for some currently popular philosophies of psychology, specifically, the "hegemony of neurophysiology view", the theories of Jerry Fodor, Daniel Dennett, and Stephen Stich, and the view that perception is permeated by belief. In the last section, I consider what the (...)
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  30. Gualtiero Piccinini & Andrea Scarantino (2010). Computation Vs. Information Processing: Why Their Difference Matters to Cognitive Science. Studies in History and Philosophy of Science Part A 41 (3):237-246.score: 96.0
    Since the cognitive revolution, it’s become commonplace that cognition involves both computation and information processing. Is this one claim or two? Is computation the same as information processing? The two terms are often used interchangeably, but this usage masks important differences. In this paper, we distinguish information processing from computation and examine some of their mutual relations, shedding light on the role each can play in a theory of cognition. We recommend that theoristError: Illegal entry in bfrange block in (...)
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  31. Steven Lehar, Computational Implications of Gestalt Theory: The Role of Feedback in Visual Processing.score: 96.0
    Neurophysiological investigations of the visual system by way of single-cell recordings have revealed a hierarchical architecture in which lower level areas, such as the primary visual cortex, contain cells that respond to simple features, while higher level areas contain cells that respond to higher order features apparently composed of combinations of lower level features. This architecture seems to suggest a feed-forward processing strategy in which visual information progresses from lower to higher visual areas. However there is other evidence, both neurophysiological (...)
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  32. Erkan Tin & Varol Akman (1994). Computational Situation Theory. ACM SIGART Bulletin 5 (4):4-17.score: 96.0
    Situation theory has been developed over the last decade and various versions of the theory have been applied to a number of linguistic issues. However, not much work has been done in regard to its computational aspects. In this paper, we review the existing approaches towards `computational situation theory' with considerable emphasis on our own research.
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  33. Marc Dymetman (1998). Group Theory and Computational Linguistics. Journal of Logic, Language and Information 7 (4):461-497.score: 96.0
    There is currently much interest in bringing together the tradition of categorial grammar, and especially the Lambek calculus, with the recent paradigm of linear logic to which it has strong ties. One active research area is designing non-commutative versions of linear logic (Abrusci, 1995; Retoré, 1993) which can be sensitive to word order while retaining the hypothetical reasoning capabilities of standard (commutative) linear logic (Dalrymple et al., 1995). Some connections between the Lambek calculus and computations in groups have long been (...)
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  34. Evan Selinger & Timothy Engström (2008). A Moratorium on Cyborgs: Computation, Cognition, and Commerce. [REVIEW] Phenomenology and the Cognitive Sciences 7 (3):327-341.score: 96.0
    By examining the contingent alliance that has emerged between the computational theory of mind and cyborg theory, we discern some questionable ways in which the literalization of technological metaphors and the over-extension of the “computational” have functioned, not only to influence conceptions of cognition, but also by becoming normative perspectives on how minds and bodies should be transformed, such that they can capitalize on technology’s capacity to enhance cognition and thus amend our sense of what it (...)
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  35. Todd Wareham, Iris van Rooij & Moritz Müller (2008). Computational Complexity Analysis Can Help, but First We Need a Theory. Behavioral and Brain Sciences 31 (4):399-400.score: 96.0
    Leech et al. present a connectionist algorithm as a model of (the development) of analogizing, but they do not specify the algorithm's associated computational-level theory, nor its computational complexity. We argue that doing so may be essential for connectionist cognitive models to have full explanatory power and transparency, as well as for assessing their scalability to real-world input domains.
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  36. Steven Horst (1996). Symbols, Computation, and Intentionality: A Critique of the Computational Theory of Mind. University of California Press.score: 96.0
    In this carefully argued critique, Steven Horst pronounces the theory deficient.
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  37. Alexander Clark, Computational Learning Theory and Language Acquisition.score: 96.0
    Computational learning theory explores the limits of learnability. Studying language acquisition from this perspective involves identifying classes of languages that are learnable from the available data, within the limits of time and computational resources available to the learner. Different models of learning can yield radically different learnability results, where these depend on the assumptions of the model about the nature of the learning process, and the data, time, and resources that learners have access to. To the extent (...)
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  38. David A. Rosenbaum (2001). Computational Motor Planning and the Theory of Event Coding. Behavioral and Brain Sciences 24 (5):902-903.score: 96.0
    Recent computational models of motor planning have relied heavily on anticipating the consequences of motor acts. Such anticipation is vital for dealing with the redundancy problem of motor control (i.e., the problem of selecting a particular motor solution when more than one is possible to achieve a goal). Computational approaches to motor planning support the Theory of Event Coding (TEC).
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  39. Terrance Tomkow, The Computational Theory of the Laws of Nature.score: 96.0
    A new account of the of the laws of nature based upon Algorithmic Information Theory.
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  40. Georg Dorffner (1998). Flexible Features, Connectionism, and Computational Learning Theory. Behavioral and Brain Sciences 21 (1):24-25.score: 96.0
    This commentary is an elaboration on Schyns, Goldstone & Thibaut's proposal for flexible features in categorization in the light of three areas not explicitly discussed by the authors: connectionist models of categorization, computational learning theory, and constructivist theories of the mind. In general, the authors' proposal is strongly supported, paving the way for model extensions and for interesting novel cognitive research. Nor is the authors' proposal incompatible with theories positing some fixed set of features.
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  41. Arthur M. Jacobs & Jonathan Grainger (1999). Modeling a Theory Without a Model Theory, or, Computational Modeling “After Feyerabend”. Behavioral and Brain Sciences 22 (1):46-47.score: 96.0
    Levelt et al. attempt to “model their theory” with WEAVER++. Modeling theories requires a model theory. The time is ripe for a methodology for building, testing, and evaluating computational models. We propose a tentative, five-step framework for tackling this problem, within which we discuss the potential strengths and weaknesses of Levelt et al.'s modeling approach.
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  42. David M. W. Powers (2006). Comparative, Continuity, and Computational Evidence in Evolutionary Theory: Predictive Evidence Versus Productive Evidence. Behavioral and Brain Sciences 29 (3):294-296.score: 96.0
    Of three types of evidence available to evolution theorists – comparative, continuity, and computational – the first is largely productive rather than predictive. Although comparison between extant species or languages is possible and can be suggestive of evolutionary processes, leading to theory development, comparison with extinct species and languages seems necessary for validation. Continuity and computational evidence provide the best opportunities for supporting predictions.
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  43. G. Basti (2014). Info-Computational Constructivism and Quantum Field Theory. Constructivist Foundations 9 (2):242-244.score: 96.0
    Open peer commentary on the article “Info-computational Constructivism and Cognition” by Gordana Dodig-Crnkovic. Upshot: Dodig-Crnkovic’s “info-computational constructivism” (IC), as an essential part of a constructivist approach, needs integration with the logical, mathematical and physical evidence coming from quantum field theory (QFT) as the fundamental physics of the emergence of “complex systems” in all realms of natural sciences.
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  44. Todd Wareham, Iris van Rooij & Moritz Müller (2008). Computational Complexity Analysis Can Help, but First We Need a Theory. Behavioral and Brain Sciences 31 (4):399-400.score: 96.0
    Leech et al. present a connectionist algorithm as a model of (the development) of analogizing, but they do not specify the algorithm's associated computational-level theory, nor its computational complexity. We argue that doing so may be essential for connectionist cognitive models to have full explanatory power and transparency, as well as for assessing their scalability to real-world input domains.
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  45. Jeffrey White (forthcoming). Manufacturing Morality A General Theory of Moral Agency Grounding Computational Implementations: The ACTWith Model. In Computational Intelligence. Nova Publications.score: 96.0
    The ultimate goal of research into computational intelligence is the construction of a fully embodied and fully autonomous artificial agent. This ultimate artificial agent must not only be able to act, but it must be able to act morally. In order to realize this goal, a number of challenges must be met, and a number of questions must be answered, the upshot being that, in doing so, the form of agency to which we must aim in developing artificial agents (...)
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  46. Keith Butler (1996). Individualism and Marr's Computational Theory of Vision. Mind and Language 11 (4):313-37.score: 94.0
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  47. Jason L. Megill & Jon Cogburn (2005). Easy's Gettin' Harder All the Time: The Computational Theory and Affective States. Ratio 18 (3):306-316.score: 94.0
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  48. Roger Fellows (1995). Welcome to Wales: Searle on the Computational Theory of Mind. In , Philosophy and Technology. New York: Cambridge University Press. 85-97.score: 94.0
  49. Jakub Szymanik (2009). Quantifiers in TIME and SPACE. Computational Complexity of Generalized Quantifiers in Natural Language. Dissertation, University of Amsterdamscore: 92.0
    In the dissertation we study the complexity of generalized quantifiers in natural language. Our perspective is interdisciplinary: we combine philosophical insights with theoretical computer science, experimental cognitive science and linguistic theories. -/- In Chapter 1 we argue for identifying a part of meaning, the so-called referential meaning (model-checking), with algorithms. Moreover, we discuss the influence of computational complexity theory on cognitive tasks. We give some arguments to treat as cognitively tractable only those problems which can be computed in (...)
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