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            Representation in Cognitive Science

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            Author(s)
            Shea, Nicholas
            Collection
            European Research Council (ERC)
            Language
            English
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            Abstract
            "Our thoughts are meaningful. We think about things in the outside world; how can that be so? This is one of the deepest questions in contemporary philosophy. Ever since the 'cognitive revolution', states with meaning-mental representations-have been the key explanatory construct of the cognitive sciences. But there is still no widely accepted theory of how mental representations get their meaning. Powerful new methods in cognitive neuroscience can now reveal information processing in the brain in unprecedented detail. They show how the brain performs complex calculations on neural representations. Drawing on this cutting-edge research, Nicholas Shea uses a series of case studies from the cognitive sciences to develop a naturalistic account of the nature of mental representation. His approach is distinctive in focusing firmly on the 'subpersonal' representations that pervade so much of cognitive science. The diversity and depth of the case studies, illustrated by numerous figures, make this book unlike any previous treatment. It is important reading for philosophers of psychology and philosophers of mind, and of considerable interest to researchers throughout the cognitive sciences."
            URI
            https://doab-dev.siscern.org/handle/20.500.12854/198581
            Keywords
            contemporary philosophy; cognitive sciences; mental representations; cognitive neuroscience; thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GT Interdisciplinary studies::GTK Cognitive studies
            DOI
            10.1093/oso/9780198812883.001.0001
            ISBN
            9780198812883
            Publisher
            Oxford University Press
            Publisher website
            http://ukcatalogue.oup.com
            Publication date and place
            Oxford, UK, 2018
            Grantor
            • H2020 European Research Council
            Pages
            304
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              This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871069.

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