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Representation, abstraction, and simple-minded sophisticates

Published online by Cambridge University Press:  19 June 2020

Peter Dayan Open the ORCID record for Peter Dayan [Opens in a new window]
Peter Dayan*
Affiliation:
Max-Planck-Gesellschaft, Max Planck-Ring 8, 72076Tübingen, Germany. [email protected] https://www.kyb.tuebingen.mpg.de/publication-search/60427?person=persons217460
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Abstract

Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 43 , 2020 , e126
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Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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References

Berger, J. (1985) Statistical decision theory and Bayesian analysis. Springer.CrossRefGoogle Scholar
Collins, A. G. E. & Frank, M. J. (2012) How much of reinforcement learning is working memory, not reinforcement learning? A behavioral, computational, and neurogenetic analysis. European Journal of Neuroscience 35:1024–35.CrossRefGoogle Scholar
Daw, N. D., Niv, Y. & Dayan, P. (2005) Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control. Nature Neuroscience 8(12):1704–711.CrossRefGoogle ScholarPubMed
Dayan, P. (1993) Improving generalization for temporal difference learning: The successor representation. Neural Computation 5:613–24.CrossRefGoogle Scholar
Gershman, S. J. & Daw, N. D. (2017) Reinforcement learning and episodic memory in humans and animals: An integrative framework. Annual Review of Psychology 68(1):101–28. https://doi.org/10.1146/annurev-psych-122414-033625.CrossRefGoogle Scholar
Goodman, N. D., Mansinghka, V. K., Roy, D. M., Bonawitz, K. & Tenenbaum, J. B. (2012) Church: A language for generative models. CoRR, abs/ 1206.3255.Google Scholar
Harsanyi, J. C. (1967) Games with incomplete information played by “Bayesian” players, I–III Part I. The basic model. Management Science 14(3):159–82.CrossRefGoogle Scholar
Hinton, G. & Sejnowski, T., ed. (1999) Unsupervised learning: Foundations of neural computation. MIT Press.CrossRefGoogle Scholar
Keramati, M., Dezfouli, A. & Piray, P. (2011) Speed/accuracy trade-off between the habitual and the goal-directed processes. PLOS Computational Biology 7:e1002055.CrossRefGoogle ScholarPubMed
Keramati, M., Smittenaar, P., Dolan, R. J. & Dayan, P. (2016) Adaptive integration of habits into depth-limited planning defines a habitual-goal-directed spectrum. Proceedings of the National Academy of Sciences 113:12868–73.CrossRefGoogle ScholarPubMed
Lengyel, M. and Dayan, P. (2007) Hippocampal contributions to control: The third way. In: Advances in Neural Information Processing Systems 20, Proceedings of the Twenty-First Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, December 3–6, 2007, pp. 889–96.Google Scholar
Littman, M. L., Sutton, R. S. & Singh, S. P. (2001) Predictive representations of state. In: Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, NIPS 2001, December 3–8, 2001, Vancouver, British Columbia, Canada], pp. 1555–61.Google Scholar
MacKay, D. (1956) The epistemological problem for automata. In: Automata studies, ed. Shannon, C. & McCarthy, J., pp. 235–51. Princeton University Press.Google Scholar
Marr, D. (1970) A theory for cerebral neocortex. Proceedings of the Royal Society B: Biological Sciences 176:161–234.Google ScholarPubMed
Neisser, U. (1967) Cognitive psychology. Appleton-Century-Crofts.Google Scholar
Pezzulo, G., Rigoli, F. & Chersi, F. (2013) The mixed instrumental controller: Using value of information to combine habitual choice and mental simulation. Frontiers in Psychology 4:92.CrossRefGoogle ScholarPubMed
Rao, R. P. N. & Ballard, D. H. (1999) Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptive-field effects. Nature Neuroscience 2(1):79–87. Available at: http://doi.org/10.1038/4580.CrossRefGoogle ScholarPubMed
Sutton, R. S. & Barto, A. G. (1998) Reinforcement learning: An introduction (adaptive computation and machine learning). The MIT Press.Google Scholar