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.score: 156.0
    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. Richard Montgomery (1998). Grades of Explanation in Cognitive Science. Synthese 114 (3):463-495.score: 150.0
    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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  3. Nick Chater (2014). Cognitive Science as an Interface Between Rational and Mechanistic Explanation. Topics in Cognitive Science 6 (1):331-337.score: 150.0
    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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  4. Jordi Fernández (2003). Explanation by Computer Simulation in Cognitive Science. Minds And Machines 13 (2):269-284.score: 144.0
    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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  5. Joel Walmsley (2008). Explanation in Dynamical Cognitive Science. Minds and Machines 18 (3):331-348.score: 144.0
    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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  6. William S. Robinson (1999). Representation and Cognitive Explanation. In Understanding Representation in the Cognitive Sciences: Does Representation Need Reality, Riegler. Dordrecht: Kluwer Academic Pub.score: 144.0
     
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  7. 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.score: 120.0
  8. Richard Montgomery (1995). Explanation and Evaluation in Cognitive Science. Philosophy of Science 62 (2):261-82.score: 110.0
    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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  9. William P. Bechtel (1994). Levels of Description and Explanation in Cognitive Science. Minds and Machines 4 (1):1-25.score: 108.0
    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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  10. Jay F. Rosenberg (1994). Comments on Bechtel, Levels of Description and Explanation in Cognitive Science. Minds and Machines 4 (1):27-37.score: 108.0
    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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  11. Elizabeth Irvine (forthcoming). Models, Robustness, and Non-Causal Explanation: A Foray Into Cognitive Science and Biology. Synthese:1-17.score: 108.0
    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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  12. Andy Clark (1998). Twisted Tales: Causal Complexity and Cognitive Scientific Explanation. [REVIEW] Minds and Machines 8 (1):79-99.score: 102.0
    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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  13. Robert N. McCauley, Levels of Explanation and Cognitive Architectures* By.score: 102.0
    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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  14. Keld Stehr Nielsen (2006). Discussions Dynamical Explanation in Cognitive Science. Journal for General Philosophy of Science 37 (1):139-163.score: 102.0
    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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  15. Robert N. McCauley (1987). The Role of Cognitive Explanations in Psychology. Behaviorism 15 (1):27-40.score: 102.0
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  16. 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.score: 96.0
    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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  17. Sasan Haghighi, The Role of Philosophy in Cognitive Science: Normativity, Generality, Mechanistic Explanation. OZSW 2013 Rotterdam.score: 96.0
    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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  18. 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.score: 96.0
    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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  19. Rainer Mausfeld (2012). On Some Unwarranted Tacit Assumptions in Cognitive Neuroscience. Frontiers in Cognition 3 (67):1-13.score: 92.0
    The cognitive neurosciences are based on the idea that the level of neurons or neural networks constitutes a privileged level of analysis for the explanation of mental phenomena. This paper brings to mind several arguments to the effect that this presumption is ill-conceived and unwarranted in light of what is currently understood about the physical principles underlying mental achievements. It then scrutinizes the question why such conceptions are nevertheless currently prevailing in many areas of psychology. The paper argues (...)
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  20. William Bechtel & Mitchell Herschbach (2010). Philosophy of the Cognitive Sciences. In Fritz Allhoff (ed.), Philosophies of the Sciences. Wiley-Blackwell. 239--261.score: 92.0
    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 (...)
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  21. Francisco Calvo Garzon & Angel Garcia Rodriguez (2009). Where is Cognitive Science Heading? Minds and Machines 19 (3):301-318.score: 90.0
    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 (...)
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  22. Carlos Zednik (2011). The Nature of Dynamical Explanation. Philosophy of Science 78 (2):238-263.score: 90.0
    The received view of dynamical explanation is that dynamical cognitive science seeks to provide covering law explanations of cognitive phenomena. By analyzing three prominent examples of dynamicist research, I show that the received view is misleading: some dynamical explanations are mechanistic explanations, and in this way resemble computational and connectionist explanations. Interestingly, these dynamical explanations invoke the mathematical framework of dynamical systems theory to describe mechanisms far more complex and distributed than the ones typically considered by philosophers. (...)
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  23. Gualtiero Piccinini (2006). Computational Explanation in Neuroscience. Synthese 153 (3):343-353.score: 90.0
    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 and (...)
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  24. Francisco Calvo Garzón & Ángel García Rodríguez (2009). Where is Cognitive Science Heading? Minds and Machines 19 (3):301-318.score: 90.0
    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 (...)
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  25. Machiel Keestra (2008). The Diverging Force of Imitation. Integrating Cognitive Science and Hermeneutics. Review of General Psychology 12 (2):127-136.score: 90.0
    Recent research on infant and animal imitation and on mirror neuron systems has
    brought imitation back in focus in psychology and cognitive science. This topic has
    always been important for philosophical hermeneutics as well, focusing on theory and
    method of understanding. Unfortunately, relations between the scientific and the
    hermeneutic approaches to imitation and understanding have scarcely been investigated,
    to the loss of both disciplines. In contrast to the cognitive scientific emphasis on
    sharing and convergence of representations, the hermeneutic analysis emphasizes the
    indeterminacy and openness of (...)
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  26. Jacqueline Anne Sullivan (2010). Realization, Explanation and the Mind-Body Relation Editor's Introduction. Synthese 177 (2):151-164.score: 90.0
    This volume brings together a number of perspectives on the nature of realization explanation and experimentation in the ‘special’ and biological sciences as well as the related issues of psychoneural reduction and cognitive extension. The first two papers in the volume may be regarded as offering direct responses to the questions: (1) What model of realization is appropriate for understanding the metaphysics of science? and (2) What kind of philosophical work is such a model ultimately supposed to do?
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  27. Frances Egan (forthcoming). Function-Theoretic Explanation and the Search for Neural Mechanisms. In David M. Kaplan (ed.), Integrating Mind and Brain Science: Mechanistic Perspectives and Beyond. Oxford University Press.score: 90.0
    A common kind of explanation in cognitive neuroscience might be called function-theoretic: with some target cognitive capacity in view, the theorist hypothesizes that the system computes a well-defined function (in the mathematical sense) and explains how computing this function contributes to the exercise of the cognitive capacity. Recently, proponents of the so-called ‘new mechanist’ approach in philosophy of science have argued that a model of a cognitive capacity is explanatory only to the extent that it (...)
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  28. Alison Gopnik (1998). Explanation as Orgasm. Minds and Machines 8 (1):101-118.score: 90.0
    I argue that explanation should be thought of as the phenomenological mark of the operation of a particular kind of cognitive system, the theory-formation system. The theory-formation system operates most clearly in children and scientists but is also part of our everyday cognition. The system is devoted to uncovering the underlying causal structure of the world. Since this process often involves active intervention in the world, in the case of systematic experiment in scientists, and play in children, the (...)
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  29. Emmanuel M. Pothos (2005). The Rules Versus Similarity Distinction. Behavioral and Brain Sciences 28 (1):1-14.score: 90.0
    The distinction between rules and similarity is central to our understanding of much of cognitive psychology. Two aspects of existing research have motivated the present work. First, in different cognitive psychology areas we typically see different conceptions of rules and similarity; for example, rules in language appear to be of a different kind compared to rules in categorization. Second, rules processes are typically modeled as separate from similarity ones; for example, in a learning experiment, rules and similarity influences (...)
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  30. Raoul Gervais & Erik Weber (2011). The Covering Law Model Applied to Dynamical Cognitive Science: A Comment on Joel Walmsley. Minds and Machines 21 (1):33-39.score: 90.0
    In a 2008 paper, Walmsley argued that the explanations employed in the dynamical approach to cognitive science, as exemplified by the Haken, Kelso and Bunz model of rhythmic finger movement, and the model of infant preservative reaching developed by Esther Thelen and her colleagues, conform to Carl Hempel and Paul Oppenheim’s deductive-nomological model of explanation (also known as the covering law model). Although we think Walmsley’s approach is methodologically sound in that it starts with an analysis of scientific (...)
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  31. G. Randolph Mayes (2000). Resisting Explanation. Argumentation 14 (4):361-380.score: 90.0
    Although explanation is widely regarded as an important concept in the study of rational inquiry, it remains largely unexplored outside the philosophy of science. This, I believe, is not due to oversight as much as to institutional resistance. In analytic philosophy it is basic that epistemic rationality is a function of justification and that justification is a function of argument. Explanation, however, is not argument nor is belief justification its function. I argue here that the task of incorporating (...)
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  32. Alexander Powell (2009). Molecules, Cells and Minds: Aspects of Bioscientific Explanation. Dissertation, University of Exeterscore: 90.0
    In this thesis I examine a number of topics that bear on explanation and understanding in molecular and cell biology, in order to shed new light on explanatory practice in those areas and to find novel angles from which to approach relevant philosophical debates. The topics I look at include mechanism, emergence, cellular complexity, and the informational role of the genome. I develop a perspective that stresses the intimacy of the relations between ontology and epistemology. Whether a phenomenon looks (...)
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  33. Machiel Keestra (2012). Bounded Mirroring. Joint Action and Group Membership in Political Theory and Cognitive Neuroscience. In Frank Vandervalk (ed.), Thinking about the Body Politic: Essays on Neuroscience and Political Theory. Routledge. 222--249.score: 90.0
    A crucial socio-political challenge for our age is how to rede!ne or extend group membership in such a way that it adequately responds to phenomena related to globalization like the prevalence of migration, the transformation of family and social networks, and changes in the position of the nation state. Two centuries ago Immanuel Kant assumed that international connectedness between humans would inevitably lead to the realization of world citizen rights (Kant 1968). Nonetheless, globalization does not just foster cosmopolitanism but simultaneously (...)
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  34. Emery Schubert, David J. Hargreaves & Adrian C. North (2014). A Dynamically Minimalist Cognitive Explanation of Musical Preference: Is Familiarity Everything? Frontiers in Psychology 5.score: 90.0
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  35. W. David Pierce & W. Frank Epling (forthcoming). On the Persistence of Cognitive Explanation: Implications for Behavior Analysis. Behaviorism.score: 90.0
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  36. Paul Smolensky (1995). Constituent Structure and Explanation in an Integrated Connectionist/Symbolic Cognitive Architecture. In C. Macdonald (ed.), Connectionism: Debates on Psychological Explanation. Blackwell.score: 90.0
     
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  37. Kelso Cratsley & Richard Samuels (2013). Cognitive Science and Explanations of Psychopathology. In K. W. M. Fulford (ed.), The Oxford Handbook of Philosophy and Psychiatry. Oxford University Press. 413.score: 84.0
  38. Max Coltheart & Martin Davies (2003). Inference and Explanation in Cognitive Neuropsychology. Cortex 39 (1):188-191.score: 78.0
    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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  39. 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.score: 78.0
    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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  40. Martin Davies, Inference and Explanation in Cognitive Neuropsychology.score: 78.0
    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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  41. Keld Stehr Nielsen (2006). Dynamical Explanation in Cognitive Science. Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 37 (1):139 - 163.score: 78.0
    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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  42. 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.score: 78.0
    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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  43. Frank C. Keil & Robert A. Wilson (2000). The Shadows and Shallows of Explanation. In Frank C. Keil & Robert A. Wilson (eds.), Explanation and Cognition. MIT Press.. 137-159.score: 78.0
    Reprinted, with modification, from Wilson and Keil 1998.
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  44. Marco Fenici (forthcoming). Social Cognitive Abilities in Infancy: Is Mindreading the Best Explanation? Philosophical Psychology:1-25.score: 78.0
    I discuss three arguments that have been advanced in support of the epistemic mentalist view, i.e., the view that infants' social cognitive abilities (SCAs) 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 (...)
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  45. William P. Bechtel (1998). Representations and Cognitive Explanations: Assessing the Dynamicist Challenge in Cognitive Science. Cognitive Science 22 (3):295-317.score: 74.0
    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 (...)
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  46. María G. Navarro & Noemi de Haro García (2012). Cognitive Abduction in the Study of Visual Culture. Philosophy and Cognitive Science. Western and Eastern Studies 2:205-220.score: 74.0
    In this paper art history and visual studies, the disciplines that study visual culture, are presented as a field whose conjectural paradigm can be used to understand the epistemic problems associated with abduction. In order to do so, significant statements, concepts and arguments from the work of several specialists in this field have been highlighted. Their analysis shows the fruitfulness and potential for understanding the study of visual culture as a field that is interwoven with the assumptions of abductive cognition.
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  47. Graham Wood (2009). Detecting Design: Fast and Frugal or All Things Considered? Sophia 48 (2):195 - 210.score: 72.0
    Within the Cognitive Science of Religion, Justin Barrett has proposed that humans possess a hyperactive agency detection device that was selected for in our evolutionary past because ‘over detecting’ (as opposed to ‘under detecting’) the existence of a predator conferred a survival advantage. Within the Intelligent Design debate, William Dembski has proposed the law of small probability, which states that specified events of small probability do not occur by chance. Within the Fine-Tuning debate, John Leslie has asserted a tidiness (...)
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  48. Achim Stephan (2006). The Dual Role of 'Emergence' in the Philosophy of Mind and in Cognitive Science. Synthese 151 (3):485-498.score: 72.0
    The concept of emergence is widely used in both the philosophy of mind and in cognitive science. In the philosophy of mind it serves to refer to seemingly irreducible phenomena, in cognitive science it is often used to refer to phenomena not explicitly programmed. There is no unique concept of emergence available that serves both purposes.
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  49. Machiel Keestra (2014). Sculpting the Space of Actions. Explaining Human Action by Integrating Intentions and Mechanisms. Dissertation, University of Amsterdamscore: 72.0
    How can we explain the intentional nature of an expert’s actions, performed without immediate and conscious control, relying instead on automatic cognitive processes? How can we account for the differences and similarities with a novice’s performance of the same actions? Can a naturalist explanation of intentional expert action be in line with a philosophical concept of intentional action? Answering these and related questions in a positive sense, this dissertation develops a three-step argument. Part I considers different methods of (...)
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  50. Max Coltheart & Robyn Langdon (1998). Autism, Modularity and Levels of Explanation in Cognitive Science. Mind and Language 13 (1):138-152.score: 72.0
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