Search results for 'Cognitive explanation' (try it on Scholar)

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  1. Jeffrey S. Poland & Barbara Von Eckardt (2004). Mechanism and Explanation in Cognitive Neuroscience. Philosophy of Science 71 (5):972-984.
    The aim of this paper is to examine the usefulness of the Machamer, Darden, and Craver (2000) mechanism approach to gaining an understanding of explanation in cognitive neuroscience. We argue that although the mechanism approach can capture many aspects of explanation in cognitive neuroscience, it cannot capture everything. In particular, it cannot completely capture all aspects of the content and significance of mental representations or the evaluative features constitutive of psychopathology.
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  2. William S. Robinson (1999). Representation and Cognitive Explanation. In Understanding Representation in the Cognitive Sciences: Does Representation Need Reality, Riegler. Dordrecht: Kluwer Academic Pub
     
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  3.  15
    Richard P. Cooper & David Peebles (2015). Beyond Single‐Level Accounts: The Role of Cognitive Architectures in Cognitive Scientific Explanation. Topics in Cognitive Science 7 (2):243-258.
    We consider approaches to explanation within the cognitive sciences that begin with Marr's computational level or Marr's implementational level and argue that each is subject to fundamental limitations which impair their ability to provide adequate explanations of cognitive phenomena. For this reason, it is argued, explanation cannot proceed at either level without tight coupling to the algorithmic and representation level. Even at this level, however, we argue that additional constraints relating to the decomposition of the (...) system into a set of interacting subfunctions are required. Integrated cognitive architectures that permit abstract specification of the functions of components and that make contact with the neural level provide a powerful bridge for linking the algorithmic and representational level to both the computational level and the implementational level. (shrink)
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  4.  72
    Richard Montgomery (1998). Grades of Explanation in Cognitive Science. Synthese 114 (3):463-495.
    I sketch an explanatory framework that fits a variety of contemporary research programs in cognitive science. I then investigate the scope and the implications of this framework. The framework emphasizes (a) the explanatory role played by the semantic content of cognitive representations, and (b) the important mechanistic, non-intentional dimension of cognitive explanations. I show how both of these features are present simultaneously in certain varieties of cognitive explanation. I also consider the explanatory role played by (...)
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  5.  28
    Nick Chater (2014). Cognitive Science as an Interface Between Rational and Mechanistic Explanation. Topics in Cognitive Science 6 (2):331-337.
    Cognitive science views thought as computation; and computation, by its very nature, can be understood in both rational and mechanistic terms. In rational terms, a computation solves some information processing problem (e.g., mapping sensory information into a description of the external world; parsing a sentence; selecting among a set of possible actions). In mechanistic terms, a computation corresponds to causal chain of events in a physical device (in engineering context, a silicon chip; in biological context, the nervous system). The (...)
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  6.  40
    Joel Walmsley (2008). Explanation in Dynamical Cognitive Science. Minds and Machines 18 (3):331-348.
    In this paper, I outline two strands of evidence for the conclusion that the dynamical approach to cognitive science both seeks and provides covering law explanations. Two of the most successful dynamical models—Kelso’s model of rhythmic finger movement and Thelen et al.’s model of infant perseverative reaching—can be seen to provide explanations which conform to the famous explanatory scheme first put forward by Hempel and Oppenheim. In addition, many prominent advocates of the dynamical approach also express the provision of (...)
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  7.  56
    Jordi Fernández (2003). Explanation by Computer Simulation in Cognitive Science. Minds and Machines 13 (2):269-284.
    My purpose in this essay is to clarify the notion of explanation by computer simulation in artificial intelligence and cognitive science. My contention is that computer simulation may be understood as providing two different kinds of explanation, which makes the notion of explanation by computer simulation ambiguous. In order to show this, I shall draw a distinction between two possible ways of understanding the notion of simulation, depending on how one views the relation in which a (...)
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  8.  6
    Tony Jackson (2014). Why the Novel Happened: A Cognitive Explanation. Philosophy and Literature 38 (1):75-93.
    In 1987, psychologist Alan Leslie published the essay “Pretense and Representation: The Origins of ‘Theory of Mind.’”1 Even after more than twenty years, this remains a benchmark essay, having been cited over seven hundred times in the PsychINFO database as of summer 2011. “Theory of mind” is the cognitive-psychological term for the human ability to attribute mental states—intentions, desires, emotions—to others. Our social being depends on this ability, which humans demonstrate from infancy, though, of course, it develops as the (...)
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  9. Dick Ruimschotel (1989). Explanation, Causation, and Psychological Theories: A Methodological Study Illustrated by an Analysis of Festinger's Theory of Cognitive Dissonance and Newell & Simon's Theory of Human Problem Solving. Swets & Zeitlinger.
  10. William P. Bechtel (1994). Levels of Description and Explanation in Cognitive Science. Minds and Machines 4 (1):1-25.
    The notion of levels has been widely used in discussions of cognitive science, especially in discussions of the relation of connectionism to symbolic modeling of cognition. I argue that many of the notions of levels employed are problematic for this purpose, and develop an alternative notion grounded in the framework of mechanistic explanation. By considering the source of the analogies underlying both symbolic modeling and connectionist modeling, I argue that neither is likely to provide an adequate analysis of (...)
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  11.  9
    Marcin Miłkowski (2016). A Mechanistic Account of Computational Explanation in Cognitive Science and Computational Neuroscience. In Vincent C. Müller (ed.), Computing and Philosophy. Springer 191-205.
    Explanations in cognitive science and computational neuroscience rely predominantly on computational modeling. Although the scientific practice is systematic, and there is little doubt about the empirical value of numerous models, the methodological account of computational explanation is not up-to-date. The current chapter offers a systematic account of computational explanation in cognitive science and computational neuroscience within a mechanistic framework. The account is illustrated with a short case study of modeling of the mirror neuron system in terms (...)
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  12. Robert N. McCauley, Levels of Explanation and Cognitive Architectures* By.
    Some controversies in cognitive science, such as arguments about whether classical or distributed connectionist architectures best model the human cognitive system, reenact long-standing debates in the philosophy of science. For millennia philosophers have pondered whether mentality can submit to scientific explanation generally and to physical explanation particularly. Recently, positive answers have gained popularity. The question remains, though, as to the analytical level at which mentality is best explained. Is there a level of analysis that is peculiarly (...)
     
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  13.  2
    W. David Pierce & W. Frank Epling (1984). On the Persistence of Cognitive Explanation: Implications for Behavior Analysis. Behaviorism 12 (1):15-27.
    Skinner has assigned the persistence of cognitive explanations to the literature of freedom and dignity. This view is challenged especially as it applies to behavioral scientists. It is argued that cognitive explanations persist because current behaviorism does not challenge cognitive epistomology; because behavior analysts have failed to provide research evidence at the level of human behavior, and finally because a science of behavior based solely on operant principles is necessarily incomplete. The implications of these problems for behavior (...)
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  14.  11
    A. Nicolás Venturelli (forthcoming). A Cautionary Contribution to the Philosophy of Explanation in the Cognitive Neurosciences. Minds and Machines:1-27.
    I propose a cautionary assessment of the recent debate concerning the impact of the dynamical approach on philosophical accounts of scientific explanation in the cognitive sciences and, particularly, the cognitive neurosciences. I criticize the dominant mechanistic philosophy of explanation, pointing out a number of its negative consequences: In particular, that it doesn’t do justice to the field’s diversity and stage of development, and that it fosters misguided interpretations of dynamical models’ contribution. In order to support these (...)
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  15.  51
    Elizabeth Irvine (2015). Models, Robustness, and Non-Causal Explanation: A Foray Into Cognitive Science and Biology. Synthese 192 (12):3943-3959.
    This paper is aimed at identifying how a model’s explanatory power is constructed and identified, particularly in the practice of template-based modeling (Humphreys, Philos Sci 69:1–11, 2002; Extending ourselves: computational science, empiricism, and scientific method, 2004), and what kinds of explanations models constructed in this way can provide. In particular, this paper offers an account of non-causal structural explanation that forms an alternative to causal–mechanical accounts of model explanation that are currently popular in philosophy of biology and (...) science. Clearly, defences of non-causal explanation are far from new (e.g. Batterman, Br J Philos Sci 53:21–38, 2002a; The devil in the details: asymptotic reasoning in explanation, reduction, and emergence, 2002b; Pincock, Noûs 41:253–275, 2007; Mathematics and scientific representation 2012; Rice, Noûs. doi:10.1111/nous.12042, 2013; Biol Philos 27:685–703, 2012), so the targets here are focused on a particular type of robust phenomenon and how strong invariance to interventions can block a range of causal explanations. By focusing on a common form of model construction, the paper also ties functional or computational style explanations found in cognitive science and biology more firmly with explanatory practices across model-based science in general. (shrink)
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  16.  86
    Richard Montgomery (1995). Explanation and Evaluation in Cognitive Science. Philosophy of Science 62 (2):261-82.
    With some regularity, cognitive scientists seem to introduce cognitive values into their explanations. After identifying examples of this practice, I sketch an account of psychological explanation that, under certain conditions, legitimizes value-laden cognitive explanations in which evaluative claims appear in the explanandum. I then present and discuss two applications of the proposed account in order to show its viability and explore its consequences.
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  17.  61
    Jay F. Rosenberg (1994). Comments on Bechtel, Levels of Description and Explanation in Cognitive Science. Minds and Machines 4 (1):27-37.
    I begin by tracing some of the confusions regarding levels and reduction to a failure to distinguish two different principles according to which theories can be viewed as hierarchically arranged — epistemic authority and ontological constitution. I then argue that the notion of levels relevant to the debate between symbolic and connectionist paradigms of mental activity answers to neither of these models, but is rather correlative to the hierarchy of functional decompositions of cognitive tasks characteristic of homuncular functionalism. Finally, (...)
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  18. Stephan Hartmann & Matteo Colombo (forthcoming). Bayesian Cognitive Science, Unification and Explanation. British Journal for the Philosophy of Science:axv036.
    It is often claimed that the greatest value of the Bayesian framework in cognitive science consists in its unifying power. Several Bayesian cognitive scientists assume that unification is obviously linked to explanatory power. But this link is not obvious, as unification in science is a heterogeneous notion, which may have little to do with explanation. While a crucial feature of most adequate explanations in cognitive science is that they reveal aspects of the causal mechanism that produces (...)
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  19. Sasan Haghighi, The Role of Philosophy in Cognitive Science: Normativity, Generality, Mechanistic Explanation. OZSW 2013 Rotterdam.
    ID: 89 / Parallel 4k: 2 Single paper Topics: Philosophy of mind, Philosophy of science Keywords: Cognitive Science, Cognitive Neuroscience, Mechanistic explanations, Reductionism, Normativity, Generality, Emerging School of Philosophers of Science. The role of philosophy in cognitive science: mechanistic explanations, normativity, generality Mohammadreza Haghighi Fard Leiden University, Netherlands, The; haghighiphil@aol.com Introduction -/- Cognitive science, as an interdisciplinary research endeavour, seeks to explain mental activities such as reasoning, remembering, language use, and problem solving, and the explanations it (...)
     
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  20.  2
    Robyn Langdon Max Coltheart (1998). Autism, Modularity and Levels of Explanation in Cognitive Science. Mind and Language 13 (1):138-152.
    Over the past century or more, cognitive neuropsychologists have discussed many of the issues raised in this volume. On the basis of this literature, we argue that autism is not a single homogeneous condition, and so can have no single cause. Instead, each of its symptoms has a cause, and the proper study of autism is the separate study of each of these symptoms and its cause. We also offer evidence to support the radical view advanced by Stoljar and (...)
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  21.  5
    Charles S. Wallis (1990). Stich, Content, Prediction, and Explanation in Cognitive Science. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1990:327 - 340.
    In this paper I consider Stich's principle of autonomy argument (From Folk Psychology To Cognitive Science) as an argument that computationalism is an incorrect approach to explanation and prediction in cognitive science. After considering the principle of autonomy argument in light of several computational systems and psychological examples, I conclude that the argument is unsound. I formulate my reasons for rejecting Stich's argument as unsound into the conjunction argument. Finally, I argue that the conjunction argument is sound, (...)
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  22. 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 (...)
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  23. Bradley Franks (1995). On Explanation in Cognitive Science: Competence, Idealization, and the Failure of the Classical Cascade. British Journal for the Philosophy of Science 46 (4):475-502.
    underpinning of the cognitive sciences. I argue, however, that it often fails to provide adequate explanations, in particular in conjunction with competence theories. This failure originates in the idealizations in competence descriptions, which either ?block? the cascade, or produce a successful cascade which fails to explain cognition.
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  24. 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 (...)
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  25. Paul Smolensky (1995). Constituent Structure and Explanation in an Integrated Connectionist/Symbolic Cognitive Architecture. In C. Macdonald (ed.), Connectionism: Debates on Psychological Explanation. Blackwell
     
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  26.  8
    Keld Stehr Nielsen (2006). Discussions Dynamical Explanation in Cognitive Science. Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 37 (1):139-163.
    Applying the concepts of dynamical systems theory to explain cognitive phenomena is still a fairly recent trend in cognitive science and its potential and consequences are not nearly mapped out. A decade ago, dynamical approaches were introduced as a paradigm shift in cognitive science and in this paper I concentrate on how to substantiate this claim. After having considered and rejected the possibility that continuous time is the crucial factor, I present Kelso’s model of a near-cognitive (...)
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  27.  20
    Raoul Gervais (2013). Non-Cognitive Values and Objectivity in Scientific Explanation: Egalitarianism and the Case of the Movius Line. Perspectives on Science 21 (4):429-452.
    In the debate about values in science, it is a time-honored tradition to distinguish between the normative question of whether non-cognitive values should play a role in science and the descriptive question of whether they in fact do so or not.1 Among philosophers of science, it is now an accepted view that the descriptive question has been settled. That is, it is no longer disputed that non-cognitive values play a role in science. Hence, all that is left to (...)
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  28.  7
    Marco Fenici (2013). Social Cognitive Abilities in Infancy: Is Mindreading the Best Explanation? Philosophical Psychology 28 (3):387-411.
    I discuss three arguments that have been advanced in support of the epistemic mentalist view, i.e., the view that infants' social cognitive abilities manifest a capacity to attribute beliefs. The argument from implicitness holds that SCAs already reflect the possession of an “implicit” and “rudimentary” capacity to attribute representational states. Against it, I note that SCAs are significantly limited, and have likely evolved to respond to contextual information in situated interaction with others. I challenge the argument from parsimony by (...)
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  29.  87
    Max Coltheart & Martin Davies (2003). Inference and Explanation in Cognitive Neuropsychology. Cortex 39 (1):188-191.
    The question posed by Dunn and Kirsner (D&K) is an instance of a more general one: What can we infer from data? One answer, if we are talking about logically valid deductive inference, is that we cannot infer theories from data. A theory is supposed to explain the data and so cannot be a mere summary of the data to be explained. The truth of an explanatory theory goes beyond the data and so is never logically guaranteed by the data. (...)
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  30.  13
    S. Delarivière & J. Frans (2015). Computational Explanation in Cognitive Sciences: The Mechanist Turn. Constructivist Foundations 10 (3):426-429.
    Upshot: The computational theory of mind has been elaborated in many different ways throughout the last decades. In Explaining the Computational Mind, Milkowski defends his view that the mind can be explained as computational through his defense of mechanistic explanation. At no point in this book is there explicit mention of constructivist approaches to this topic. We will, nevertheless, argue that it is interesting for constructivist readers.
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  31. Martin Davies, Inference and Explanation in Cognitive Neuropsychology.
    The question posed by Dunn and Kirsner (D&K) is an instance of a more general one: What can we infer from data? One answer, if we are talking about logically valid deductive inference, is that we cannot infer theories from data. A theory is supposed to explain the data and so cannot be a mere summary of the data to be explained. The truth of an explanatory theory goes beyond the data and so is never logically guaranteed by the data. (...)
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  32.  12
    Keld Stehr Nielsen (2006). Dynamical Explanation in Cognitive Science. Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 37 (1):139 - 163.
    Applying the concepts of dynamical systems theory to explain cognitive phenomena is still a fairly recent trend in cognitive science and its potential and consequences are not nearly mapped out. A decade ago, dynamical approaches were introduced as a paradigm shift in cognitive science and in this paper I concentrate on how to substantiate this claim. After having considered and rejected the possibility that continuous time is the crucial factor, I present Kelso's model of a near-cognitive (...)
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  33. Mark Rowlands (1988). Anomalism, Supervenience, and Explanation in Cognitive Psychology. Dissertation, University of Oxford (United Kingdom)
    Available from UMI in association with The British Library. Requires signed TDF. ;This thesis defends the claim that the principle of methodological solipsism can play no role in the formation of the theories of cognitive psychology. Corresponding to this negative claim, but assuming a comparatively minor role, will be the positive claim that a scientific psychology ought to deal in explanations which relate mental states in virtue of their semantic contents. ;The basis of the case against methodological solipsism is (...)
     
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  34. Patrik Sã¶Rqvist (2015). On Interpretation of the Effects of Noise on Cognitive Performance: The Fallacy of Confusing the Definition of an Effect with the Explanation of That Effect. Frontiers in Psychology 6.
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  35.  74
    Wesley Buckwalter (2013). The Cognitive Science of Science: Explanation, Discovery, and Conceptual Change, by Paul Thagard. [REVIEW] Mind 122 (488):1201-1204.
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  36.  44
    Paolo C. Biondi (2013). Thagard, Paul., The Cognitive Science of Science: Explanation, Discovery, and Conceptual Change. Review of Metaphysics 66 (3):605-607.
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  37.  56
    Max Coltheart & Robyn Langdon (1998). Autism, Modularity and Levels of Explanation in Cognitive Science. Mind and Language 13 (1):138-152.
  38.  20
    Alex Levine (2010). Thomas Kuhn's Cottage Fred d'Agostino ,Naturalizing Epistemology: Thomas Kuhn and the Essential Tension(London: Palgrave Macmillan, 2010) Edwin H.-C. Hung ,Beyond Kuhn: Scientific Explanation, Theory Structure, Incommensurability and Physical Necessity(Hants: Ashgate, 2006) Hanne Andersen , Peter Barker , and Xiang Chen ,The Cognitive Structure of Scientific Revolutions(Cambridge: Cambridge University Press, 2006). [REVIEW] Perspectives on Science 18 (3):369-377.
  39.  45
    Robert C. Cummins (1991). The Role of Representation in Connectionist Explanation of Cognitive Capacities. In William Ramsey, Stephen P. Stich & D. Rumelhart (eds.), Philosophy and Connectionist Theory. Lawrence Erlbaum 91--114.
  40.  8
    Wentzel Huyssteen (1988). Experience and Explanation: The Justification of Cognitive Claims in Theology. Zygon 23 (3):247-260.
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  41.  21
    Eran Asoulin (2013). Paul Thagard , The Cognitive Science of Science: Explanation, Discovery, and Conceptual Change . Reviewed By. Philosophy in Review 33 (5):415-417.
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  42. Stewart Shapiro (2012). Objectivity, Explanation, and Cognitive Shortfall. In Crispin Wright & Annalisa Coliva (eds.), Mind, Meaning, and Knowledge: Themes From the Philosophy of Crispin Wright. Oxford University Press
     
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  43.  9
    Barbara Eckardvont & Jeffrey S. Poland (2004). Mechanism and Explanation in Cognitive Neuroscience. Philosophy of Science 71 (5):972-984.
  44.  3
    Stanley Shostak & Marcia Landy (2014). The Cognitive Science of Science: Explanation, Discovery, and Conceptual Change. The European Legacy 19 (4):525-526.
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  45.  33
    Steven Horst (2005). Modeling, Localization and the Explanation of Phenomenal Properties: Philosophy and the Cognitive Sciences at the Beginning of the Millennium. Synthese 147 (3):477-513.
    Case studies in the psychophysics, modeling and localization of human vision are presented as an example of.
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  46.  18
    Chi Chienchih (2005). A Cognitive Analysis of Confucian Self-Knowledge: According to Tu Weiming's Explanation. Dao: A Journal of Comparative Philosophy 4 (2):267-282.
  47.  5
    J. Moore (2003). Explanation and Description in Traditional Neobehaviorism, Cognitive Psychology, and Behavior Analysis. In Kennon A. Lattal (ed.), Behavior Theory and Philosophy. Springer 13--39.
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  48.  11
    Anthony Brueckner (1998). Realism, Best Explanation, and Cognitive Command. Philosophical Papers 27 (1):69-78.
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  49.  4
    John Drury (1994). Cognitive Science and Hermeneutic Explanation: Symbiotic or Incompatible Frameworks? Philosophy, Psychiatry, and Psychology 1 (1):41-50.
  50. Dominik Becker, Tilo Beckers, Simon Tobias Franzmann & Jörg Hagenah (2016). Contextualizing Cognitive Consonance by a Social Mechanisms Explanation: Moderators of Selective Exposure in Media Usage. Analyse & Kritik 38 (1).
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