Hostname: page-component-8448b6f56d-wq2xx Total loading time: 0 Render date: 2024-04-23T22:50:27.668Z Has data issue: false hasContentIssue false
  • English
  • Français

More dynamical and more symbiotic: Cortico-striatal models of resolve, suppression, and routine habit

Published online by Cambridge University Press:  26 April 2021

Linus Ta-Lun Huang Open the ORCID record for Linus Ta-Lun Huang [Opens in a new window]
Linus Ta-Lun Huang*
Affiliation:
Department of Philosophy, University of California, San Diego, La Jolla, CA92093linusthuang@gmail.com Philosophy Program, Institute of European and American Studies, Academia Sinica, Taipei11529, Taiwan.
Get access Rights & Permissions [Opens in a new window]

Abstract

I extend Ainslie's core claims with three cortico-striatal models that respectively subserve the key constructs of resolve, suppression, and routine habit. I show that these models suggest a more dynamical and symbiotic relation among the constructs: there are more ways they interact to reinforce willpower, and the temporal dimension of the interactions can often determine the effectiveness of the reinforcement.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 44 , 2021 , e41
Check for updates
Creative Commons
The target article and response article are works of the U.S. Government and are not subject to copyright protection in the United States.
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

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

Dayan, P., & Berridge, K. C. (2014). Model-based and model-free Pavlovian reward learning: Revaluation, revision, and revelation. Cognitive, Affective, & Behavioral Neuroscience, 14(2), 473–492. https://doi.org/10.3758/s13415-014-0277-8.CrossRefGoogle ScholarPubMed
Dolan, R. J., & Dayan, P. (2013). Goals and habits in the brain. Neuron, 80(2), 312–325. https://doi.org/10.1016/j.neuron.2013.09.007.CrossRefGoogle Scholar
Gershman, S. J., Daw, N. D., Rabinovich, M. I., Friston, K. J., & Varona, P. (2012). Perception, action and utility: The tangled skein. In M. I. Rabinovich, K. J. Friston, & P. Varona, (Ed.), Principles of brain dynamics: Global state interactions (pp. 293–312). MIT Press.Google Scholar
Hélie, S., Ell, S. W., & Ashby, F. G. (2015). Learning robust cortico-cortical associations with the basal ganglia: An integrative review. Cortex, 64, 123–135. https://doi.org/10.1016/j.cortex.2014.10.011.CrossRefGoogle Scholar
Huang, L. T.-L. (2017). Neurodemocracy: Self-Organization of the Embodied Mind (Ph.D. dissertation). University of Sydney.Google Scholar
Ito, M., & Doya, K. (2011). Multiple representations and algorithms for reinforcement learning in the cortico-basal ganglia circuit. Current Opinion in Neurobiology, 21(3), 368–373. https://doi.org/10.1016/j.conb.2011.04.001.CrossRefGoogle ScholarPubMed
Minsky, M. L. (1986). The society of mind. Simon and Schuster.Google Scholar
Wiecki, T. V., & Frank, M. J. (2013). A computational model of inhibitory control in frontal cortex and basal ganglia. Psychological Review, 120(2), 329–355. https://doi.org/10.1037/a0031542.CrossRefGoogle ScholarPubMed