Effort and substance use: differentiating tobacco use through reinforcement learning of effort based decision making
5 affiliations
- 1Department of Translational Neuroscience, Wake Forest University School of Medicine, Winston-Salem, NC USA
- 2Neuroscience Graduate Program, Wake Forest University Graduate School of Arts and Sciences, Winston-Salem, NC USA
- 3Department of Psychiatry, University of Arkansas for Medical Sciences, Little Rock, AR USA
- 4Department of Biology, Wake Forest University, Winston-Salem, NC USA
- 5Department of Neurosurgery, Wake Forest University School of Medicine, Winston-Salem, NC USA
Abstract
Effort-based decision making evaluates rewards relative to the effort required to obtain it, an important process of healthy goal-directed motivation and behavior. Computational models provide mechanistic insights underlying choice behavior and potential alterations in neuropsychiatric disorders, including substance use disorders. We applied computational models to effort-based choice behavior to characterize underlying decision processes and if these mechanisms differ by substance use status. Participants completed the Effort Expenditure for Rewards Task, choosing between low- and high-effort options for monetary rewards varying in magnitude and probability. Participants met criteria for no tobacco use (n = 23), current tobacco use disorder (n = 26), former tobacco use disorder (n = 22), and tobacco and opioid use disorder (n = 29). Computational models from two families, Subjective Value and Reinforcement Learning, were fit and compared. Parameters from the best-fitting model underwent principal components analysis and linear discriminant analysis. Temporal difference reinforcement learning model demonstrated greater model evidence and predictive accuracy, indicating better fit to effort-based choice behavior. Principal components analysis revealed meaningful multivariate distinctions: PC1 differentiated all groups except individuals without tobacco use versus individuals with current tobacco use disorder; PC3 distinguished tobacco and opioid use disorder from all other groups. Linear discriminant analysis demonstrated group separation with 84% classification accuracy. A reinforcement learning framework better explained participants’ effort-based choice behavior. Substance use status relates to dynamic behavioral changes (i.e. learning) as measured by the multivariate combination of learning rate, future discounting, and choice temperature.
Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1007/s10827-026-00953-6.
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BibTeX
@article{spry2026effort,
title = {{Effort and substance use: differentiating tobacco use through reinforcement learning of effort based decision making}},
author = {Kasey P. Spry and Jazmyne James and Alison H. Oliveto and Michael Mancino and Kenneth T. Kishida and Merideth A. Addicott},
journal = {Journal of Computational Neuroscience},
year = {2026},
month = aug,
doi = {10.1007/s10827-026-00953-6},
url = {https://doi.org/10.1007/s10827-026-00953-6},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Effort and substance use: differentiating tobacco use through reinforcement learning of effort based decision making
AU - Kasey P. Spry
AU - Jazmyne James
AU - Alison H. Oliveto
AU - Michael Mancino
AU - Kenneth T. Kishida
AU - Merideth A. Addicott
PY - 2026
DA - 2026/08/28
JO - Journal of Computational Neuroscience
DO - 10.1007/s10827-026-00953-6
UR - https://doi.org/10.1007/s10827-026-00953-6
AB - Effort-based decision making evaluates rewards relative to the effort required to obtain it, an important process of healthy goal-directed motivation and behavior. Computational models provide mechanistic insights underlying choice behavior and potential alterations in neuropsychiatric disorders, including substance use disorders. We applied computational models to effort-based choice behavior to characterize underlying decision processes and if these mechanisms differ by substance use status. Participants completed the Effort Expenditure for Rewards Task, choosing between low- and high-effort options for monetary rewards varying in magnitude and probability. Participants met criteria for no tobacco use (n = 23), current tobacco use disorder (n = 26), former tobacco use disorder (n = 22), and tobacco and opioid use disorder (n = 29). Computational models from two families, Subjective Value and Reinforcement Learning, were fit and compared. Parameters from the best-fitting model underwent principal components analysis and linear discriminant analysis. Temporal difference reinforcement learning model demonstrated greater model evidence and predictive accuracy, indicating better fit to effort-based choice behavior. Principal components analysis revealed meaningful multivariate distinctions: PC1 differentiated all groups except individuals without tobacco use versus individuals with current tobacco use disorder; PC3 distinguished tobacco and opioid use disorder from all other groups. Linear discriminant analysis demonstrated group separation with 84% classification accuracy. A reinforcement learning framework better explained participants’ effort-based choice behavior. Substance use status relates to dynamic behavioral changes (i.e. learning) as measured by the multivariate combination of learning rate, future discounting, and choice temperature. Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1007/s10827-026-00953-6.
SN - 0929-5313
SN - 1573-6873
ER -