Search results for 'cogsci' (try it on Scholar)

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  1.  1
    E. Zimmermann, M. F. Peschl & B. Römmer-Nossek (2010). Constructivist Curriculum Design for the Interdisciplinary Study Programme MEi:CogSci – A Case Study. Constructivist Foundations 5 (3):144-157.
    Context: Cognitive science, as an interdisciplinary research endeavour, poses challenges for teaching and learning insofar as the integration of various participating disciplines requires a reflective approach, considering and making explicit different epistemological attitudes and hidden assumptions and premises. Only few curricula in cognitive science face this integrative challenge. Problem: The lack of integrative activities might result from different challenges for people involved in truly interdisciplinary efforts, such as discussing issues on a conceptual level, negotiating colliding frameworks or sets of premises, (...)
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  2.  1
    Karl F. MacDorman & Hiroshi Ishiguro (2006). Toward Social Mechanisms of Android Science: A CogSci 2005 Workshop: 25 and 26 July 2005, Stresa, Italy. Interaction Studiesinteraction Studies Social Behaviour and Communication in Biological and Artificial Systems 7 (2):289-296.
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  3. Wayne D. Gray & Christian D. Schunn (eds.) (2002). CogSci 2002, 24th Annual Meeting of the Cognitive Science Society. Lawrence Erlbaum.
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  4. Bruce Mangan (1999). Berkeley CA, USA. Email: Mangan@ Cogsci. Berkeley. Edu. In J. Shear & Francisco J. Varela (eds.), The View From Within: First-Person Approaches to the Study of Consciousness. Imprint Academic 249.
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  5.  6
    Peschl M. F. Zimmermann E. & B. Römmer-Nossek (2010). Constructivist Curriculum Design for the Interdisciplinary Study Programme MEi:CogSci -- A Case Study. Constructivist Foundations 5 (3):144--157.
    Context: Cognitive science, as an interdisciplinary research endeavour, poses challenges for teaching and learning insofar as the integration of various participating disciplines requires a reflective approach, considering and making explicit different epistemological attitudes and hidden assumptions and premises. Only few curricula in cognitive science face this integrative challenge. Problem: The lack of integrative activities might result from different challenges for people involved in truly interdisciplinary efforts, such as discussing issues on a conceptual level, negotiating colliding frameworks or sets of premises, (...)
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  6. Zenon W. Pylyshyn (1984). Computation and Cognition. MIT Press.
  7.  36
    Athanassios Raftopoulos & Vincent C. Müller (2002). Deictic Codes, Demonstratives, and Reference: A Step Toward Solving the Grounding Problem. In Wayne D. Gray & Christian D. Schunn (eds.), CogSci 2002, 24th annual meeting of the Cognitive Science Society. Lawrence Erlbaum 762-767.
    In this paper we address the issue of grounding for experiential concepts. Given that perceptual demonstratives are a basic form of such concepts, we examine ways of fixing the referents of such demonstratives. To avoid ‘encodingism’, that is, relating representations to representations, we postulate that the process of <span class='Hi'>reference</span> fixing must be bottom-up and nonconceptual, so that it can break the circle of conceptual content and touch the world. For that purpose, an appropriate causal relation between representations and (...)
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  8.  99
    Stephen Asma, Jaak Panksepp, Rami Gabriel & Glennon Curran (2012). Philosophical Implications of Affective Neuroscience. Journal of Consciousness Studies 19 (3-4):6-48.
    These papers are based on a Symposium at the COGSCI Conference in 2010. 1. Naturalizing the Mammalian Mind (Jaak Panksepp) 2. Modularity in Cognitive Psychology and Affective Neuroscience (Rami Gabriel) 3. Affective Neuroscience and the Philosophy of Self (Stephen Asma and Tom Greif) 4. Affective Neuroscience and Law (Glennon Curran and Rami Gabriel).
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  9. Daniel Andler (2006). Phenomenology in Cognitive Science and Artificial Intelligence. In H. Dreyfus & M. Wrathall (eds.), A Companion to Phenomenology and Existentialism.
    Fifty years before the present volume appeared, artificial intelligence (AI) and cognitive science (Cogsci) emerged from a couple of small-scale academic encounters on the East Coast of the United States. Wedded together like Siamese twins, these nascent research programs appeared to rest on some general assumptions regarding the human mind, and closely connected methodological principles, which set them at such a distance from phenomenology that no contact between the two approaches seemed conceivable. Soon however contact was made, in the (...)
     
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