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The importance of constraints on constraints

Published online by Cambridge University Press:  11 March 2020

Christopher J. Bates ,
Chris R. Sims  and
Robert A. Jacobs Open the ORCID record for Robert A. Jacobs [Opens in a new window]
Christopher J. Bates
Affiliation:
Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY14627. [email protected]@ur.rochester.eduhttp://www2.bcs.rochester.edu/sites/cbates/http://www2.bcs.rochester.edu/sites/jacobslab/
Chris R. Sims
Affiliation:
Department of Cognitive Science, Rensselaer Polytechnic Institute, Troy, NY12180. [email protected]://www.cogsci.rpi.edu/~simsc3/contact.html
Robert A. Jacobs
Affiliation:
Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY14627. [email protected]@ur.rochester.eduhttp://www2.bcs.rochester.edu/sites/cbates/http://www2.bcs.rochester.edu/sites/jacobslab/
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Abstract

The “resource-rational” approach is ambitious and worthwhile. A shortcoming of the proposed approach is that it fails to constrain what counts as a constraint. As a result, constraints used in different cognitive domains often have nothing in common. We describe an alternative framework that satisfies many of the desiderata of the resource-rational approach, but in a more disciplined manner.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 43 , 2020 , e3
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Copyright
Copyright © Cambridge University Press 2020

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References

Bates, C. J. & Jacobs, R. A. (2019) Efficient data compression leads to categorical bias in perception and perceptual memory. In: Proceedings of the 41st Annual Meeting of the Cognitive Science Society, July 24–27, Montreal, Canada.Google Scholar
Bates, C. J., Lerch, R. A., Sims, C. R. & Jacobs, R. A. (2019) Adaptive allocation of human visual working memory capacity during statistical and categorical learning. Journal of Vision 19(2):11, 1–23.CrossRefGoogle ScholarPubMed
Botvinick, M., Weinstein, A., Solway, A. & Barto, A. (2015) Reinforcement learning, efficient coding, and the statistics of natural tasks. Current Opinion in Behavioral Sciences 5:71–77.CrossRefGoogle Scholar
Lerch, R. A. & Sims, C. R. (2019) Rate-distortion theory and computationally rational reinforcement learning. In: Proceedings of Reinforcement Learning and Decision Making (RLDM) 2019, July 7–10, Montreal, Canada.Google Scholar
Sims, C. R. (2016) Rate-distortion theory and human perception. Cognition 152:181–98. doi:10.1016/j.cognition.2016.03.020.CrossRefGoogle ScholarPubMed
Sims, C. R. (2018) Efficient coding explains the universal law of generalization in human perception. Science 360:6389, 652–56.CrossRefGoogle ScholarPubMed
Sims, C. R., Jacobs, R. A. & Knill, D. C. (2012) An ideal observer analysis of visual working memory. Psychological Review 119(4):807–30. doi:10.1037/a0029856.CrossRefGoogle ScholarPubMed