Journal article
Computational approaches to habits in a model-free world
Current opinion in behavioral sciences, Vol.20, pp.104-109
04/01/2018
DOI: 10.1016/j.cobeha.2017.12.001
Abstract
Model-free (MF) reinforcement learning (RL) algorithms account for a wealth of neuroscientific and behavioral data pertinent to habits; however, conspicuous disparities between model-predicted response patterns and experimental data have exposed the inadequacy of MF-RL to fully capture the domain of habitual behavior. We review several extensions to generic MF-RL algorithms that could narrow the gap between theory and empirical data. We discuss insights gained from extending RL algorithms to operate in complex environments with multidimensional continuous state spaces. We also review recent advances in hierarchical RL and their potential relevance to habits. Neurobiological evidence suggests that similar mechanisms for habitual learning and control may apply across diverse psychological domains.
Details
- Title: Subtitle
- Computational approaches to habits in a model-free world
- Creators
- Wolfgang M. Pauli - California Institute of TechnologyJeffrey Cockburn - California Institute of TechnologyEva R. Pool - California Institute of TechnologyOmar D. Perez - California Institute of TechnologyJohn P. O'Doherty - California Institute of Technology
- Resource Type
- Journal article
- Publication Details
- Current opinion in behavioral sciences, Vol.20, pp.104-109
- Publisher
- Elsevier
- DOI
- 10.1016/j.cobeha.2017.12.001
- ISSN
- 2352-1546
- eISSN
- 2352-1554
- Number of pages
- 6
- Grant note
- 1R01DA040011-01A1 / NIDA-NIH R01 grant
- Language
- English
- Date published
- 04/01/2018
- Academic Unit
- Psychological and Brain Sciences
- Record Identifier
- 9984696756102771
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