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Representation in Cognitive Science: Author’s Reply

Citation: Shea, Nicholas (2021) Representation in Cognitive Science: Author’s Reply. Studies in History and Philosophy of Science .

It is a rare privilege to have such eminent and insightful reviewers. Their kind words about the book are much appreciated – perhaps more than they realise. And I’m grateful to all three for having read the book so constructively. Each has given me several things to think about. In the space available here I will focus on the objections that seem most critical. Robert Rupert argues that I rely on an overly narrow understanding of what the cognitive sciences explain (§1). Elisabeth Camp presses me on what precisely it takes to qualify as a structural representation and raises questions about holism (§2). John Krakauer makes a fundamental objection to positing representations when they seem not to be needed to explain behaviour (§3). Rupert has also provided a useful introduction to the book, so I will jump straight in with my replies

Creators: Shea, Nicholas (0000-0002-2032-5705) and
Subjects: Philosophy
Divisions: Institute of Philosophy
Collections: London Philosophy Papers
  • 31 May 2021 (accepted)
References: Butlin, P. (2021). Cognitive Models Are Distinguished by Content, Not Format. Philosophy of Science, 88(1), 83-102. Camp, E. (2015). Logical Concepts and Associative Characterizations. In E. Margolis & S. Laurence (Eds.), Conceptual mind: New directions in the study of concepts.(pp. 591-621). London / Cambridge MA: MIT Press. Godfrey-Smith, P. (2017). Senders, receivers, and symbolic artifacts. Biological Theory, 12(4), 275-286. Hadjiosif, A. M., Krakauer, J. W., & Haith, A. M. (2021). Did we get sensorimotor adaptation wrong? Implicit adaptation as direct policy updating rather than forward-model-based learning. Journal of Neuroscience, 41(12), 2747-2761. Kriegeskorte, N., & Diedrichsen, J. (2019). Peeling the onion of brain representations. Annual review of neuroscience, 42, 407-432. Liu, Y., Mattar, M., Behrens, T., et al. (2020). Experience replay supports non-local learning. bioRxiv. Momennejad, I., Russek, E. M., Cheong, J. H., et al. (2017). The successor representation in human reinforcement learning. Nature Human Behaviour, 1(9), 680-692. Shea, N. (2015). Distinguishing Top-Down From Bottom-Up Effects’. In S. Biggs, M. Matthen, & D. Stokes (Eds.), Perception and Its Modalities (pp. 73-91). Oxford: OUP. Shea, N. (2018). Representation in cognitive science. Oxford: Oxford University Press. Shea, N., & Frith, C. D. (2016). Dual-process theories and consciousness: the case for ‘Type Zero’cognition. Neuroscience of consciousness, 2016(1). Shea, N., Krug, K., & Tobler, P. N. (2008). Conceptual representations in goal-directed decision making. Cognitive, Affective, & Behavioral Neuroscience, 8(4), 418-428. Usher, M. (2001). A Statistical Referential Theory of Content: Using Information Theory to Account for Misrepresentation. Mind & Language, 16(3), 311-334. Whittington, J. C., Muller, T. H., Mark, S., et al. (2020). The Tolman-Eichenbaum machine: Unifying space and relational memory through generalization in the hippocampal formation. Cell, 183(5), 1249-1263. e1223.


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