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From Fly Detectors to Action Control: Representations in Reinforcement Learning

Published online by Cambridge University Press:  01 January 2022

Anna-Mari Rusanen ,
Otto Lappi ,
Jami Pekkanen  and
Jesse Kuokkanen
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Abstract

According to radical enactivists, cognitive sciences should abandon the representational framework. Perceptuomotor cognition and action control are often provided as paradigmatic examples of nonrepresentational cognitive phenomena. In this article, we illustrate how motor and action control are studied in research that uses reinforcement learning algorithms. Crucially, this approach can be given a representational interpretation. Hence, reinforcement learning provides a way to explicate action-oriented views of cognitive systems in a representational way.

Type
Cognitive Sciences
Information
Philosophy of Science , Volume 88 , Issue 5 , December 2021 , pp. 1045 - 1054
Copyright
Copyright 2021 by the Philosophy of Science Association. All rights reserved.

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References

Botvinick, Matthew, Weinstein, Ari, Solway, Alec, and Barto, Andrew G.. 2015. “Reinforcement Learning, Efficient Coding, and the Statistics of Natural Tasks.” Current Opinion in Behavioral Sciences 5:71–77.CrossRefGoogle Scholar
Dayan, Peter, and Niv, Yael. 2008. “Reinforcement Learning: The Good, the Bad and the Ugly.” Current Opinion in Neurobiology 18 (2): 185–96.CrossRefGoogle ScholarPubMed
Doya, Kenji. 1999. “What Are the Computations of the Cerebellum, the Basal Ganglia and the Cerebral Cortex?” Neural Networks 12:961–74.CrossRefGoogle ScholarPubMed
Doya, Kenji. 2008. “Modulators of Decision Making.” Nature Neuroscience 11 (4): 410–16.10.1038/nn2077CrossRefGoogle ScholarPubMed
Dretske, Fred. 1981. Knowledge and the Flow of Information. Cambridge, MA: MIT Press.Google Scholar
Egan, Frances. 2014. “How to Think about Mental Content.” Philosophical Studies 170:115–35.CrossRefGoogle Scholar
Egan, Frances. 2020. “A Deflationary Account of Mental Representation.” In What Are Mental Representations? ed. Smortchkova, Joulia, Dołęga, Krzysztof, and Schlicht, Tobias, chap. 2. New York: Oxford University Press.Google Scholar
Fodor, Jerry. 1992. A Theory of Content and Other Essays. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Grush, Rick. 2004. “The Emulation Theory of Representation: Motor Control, Imagery, and Perception.” Behavioral and Brain Sciences 27 (3): 377–96.CrossRefGoogle ScholarPubMed
Hubel, David H., and Wiesel, Torsten N.. 1959. “Receptive Fields of Single Neurones in the Cat’s Striate Cortex.” Journal of Physiology 124 (3): 574–91.Google Scholar
Hubel, David H., and Wiesel, Torsten N.. 1962. “Receptive Fields, Binocular Interaction and Functional Architecture in the Cat’s Visual Cortex.” Journal of Physiology 160 (1): 106–54.10.1113/jphysiol.1962.sp006837CrossRefGoogle ScholarPubMed
Hutto, Daniel. 2015. “Overly Enactive Imagination? Radically Re-imagining Imagining.” Southern Journal of Philosophy 53:68–89.CrossRefGoogle Scholar
Hutto, Daniel, and Myin, Erik. 2013. Radicalizing Enactivism: Basic Minds without Content. Cambridge, MA: MIT Press.Google Scholar
Hutto, Daniel, and Myin, Erik. 2017. Evolving Enactivism: Basic Minds Meet Content. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Hutto, Daniel, and Myin, Erik. 2020. “Deflating Deflationism about Mental Representation.” In What Are Mental Representations? ed. Smortchkova, Joulia, Dołęga, Krzysztof, and Schlicht, Tobias, chap. 4. New York: Oxford University Press.Google Scholar
Kawato, Mitsuo. 1999. “Internal Models for Motor Control and Trajectory Planning.” Current Opinions in Neurobiology 9 (6): 718–27.CrossRefGoogle ScholarPubMed
Lettvin, Jerome Ysroael, Maturana, Humberto Romersín, McCulloch, Warren Sturgis, and Pitts, Walter Harry. 1959. “What the Frog’s Eye Tells the Frog’s Brain.” Proceedings of the IRE 47:1940–51.10.1109/JRPROC.1959.287207CrossRefGoogle Scholar
Miall, R. Christopher, and Wolpert, Daniel. 1996. Forward Models for Physiological Motor Control.” Neural Networks 9:1265–79.CrossRefGoogle Scholar
Millikan, Ruth. 1989. “Biosemantics.” Journal of Philosophy 86:281–97.CrossRefGoogle Scholar
Myin, Erik, and Hutto, Daniel. 2015. “REC: Just Radical Enough.” Studies in Logic, Grammar and Rhetoric 41 (54): 61–71.CrossRefGoogle Scholar
Niv, Yael. 2009. “Reinforcement Learning in the Brain.” Journal of Mathematical Psychology 53 (3): 139–54.CrossRefGoogle Scholar
Ramsey, William. 2007. Representation Reconsidered. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Sutton, Richard, and Barto, Andrew. 2018. Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press.Google Scholar
Thorndike, Edward. 1911. Animal Intelligence. Darien, CT: Hafner.Google Scholar
Weinstein, Ari, and Botvinick, Matthew. 2017. “Structure Learning in Motor Control: A Deep Reinforcement Learning Model.” arXiv, Cornell Univeristy. https://arxiv.org/abs/1706.06827.Google Scholar
Wolpert, Daniel, Ghahramani, Zoubin, and Jordan, Michael I.. 1995. “An Internal Model for Sensorimotor Integration.” Science 269 (5232): 1880–82.10.1126/science.7569931CrossRefGoogle ScholarPubMed