The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Addiction as a Computational Process Gone Awry
Abstract
Addictive drugs have been hypothesized to access the same neurophysiological mechanisms as natural learning systems. These natural learning systems can be modeled through temporal-difference reinforcement learning (TDRL), which requires a reward-error signal that has been hypothesized to be carried by dopamine. TDRL learns to predict reward by driving that reward-error signal to zero. By adding a noncompensable drug-induced dopamine increase to a TDRL model, a computational model of addiction is constructed that over-selects actions leading to drug receipt. The model provides an explanation for important aspects of the addiction literature and provides a theoretic view-point with which to address other aspects.
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Cites
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Cited by
NEURAL MECHANISMS OF ADDICTION: The Role of Reward-Related Learning and Memory
Hyman, S. E., Malenka, R. C., & Nestler, E. J. (2006) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.29.051605.113009 · Cited by 2,647
The debate over dopamine’s role in reward: the case for incentive salience
Berridge, K. C. (2007) · Psychopharmacology · doi:10.1007/s00213-006-0578-x · Cited by 2,347
A framework for studying the neurobiology of value-based decision making
Rangel, A., Camerer, C., & Montague, P. R. (2008) · Nature Reviews Neuroscience · doi:10.1038/nrn2357 · Cited by 2,184
Habits, Rituals, and the Evaluative Brain
Graybiel, A. M. (2008) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.29.051605.112851 · Cited by 1,697
Paulus, M. P., & Stein, M. B. (2006) · Biological Psychiatry · doi:10.1016/j.biopsych.2006.03.042 · Cited by 1,325
Metadata
- Authors
- Redish, A. D.
- Venue
- Science
- Published
- 2004-12-10
- Volume
- 306
- Issue
- 5703
- Pages
- 1944-1947
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2119631764
- Cited by
- 540
- References
- 14
- FWCI
- 8.31
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
