Hostname: page-component-8448b6f56d-jr42d Total loading time: 0 Render date: 2024-04-23T20:08:58.644Z Has data issue: false hasContentIssue false

The importance of constraints on constraints

Published online by Cambridge University Press:  11 March 2020

Christopher J. Bates
Affiliation:
Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY14627. cjbates@ur.rochester.edurjacobs@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. simsc3@rpi.eduhttp://www.cogsci.rpi.edu/~simsc3/contact.html
Robert A. Jacobs
Affiliation:
Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY14627. cjbates@ur.rochester.edurjacobs@ur.rochester.eduhttp://www2.bcs.rochester.edu/sites/cbates/http://www2.bcs.rochester.edu/sites/jacobslab/

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
Copyright
Copyright © Cambridge University Press 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

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 5:7177.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