ReviewJournal of abnormal psychology2020
Computational models of drug use and addiction: A review.
Review in Journal of abnormal psychology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
26 citing papers in PubMed, 61 citations in OpenAlex.
- Psilocybin-induced changes in neural reactivity to alcohol and emotional cues in patients with alcohol use disorder: an fMRI pilot study.Scientific reports · 2024Trial
- Neurobehavioral differences in value-based decision making between people with cocaine use disorder and controls.Experimental and clinical psychopharmacology · 2026Article
- Rapid and reliable computational markers of decision-making for predicting daily smoking behavior and smoking cessation treatment outcomes.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Article
- Computational mechanisms of learning and forgetting differentiate affective and substance use disorders.Molecular psychiatry · 2026Article
- The informational dysregulation framework of addiction (IDFA): an information-processing model of relapse in opioid use disorder.Frontiers in psychiatry · 2026Article
- Quantitative Economon Model of Transactions for Drugs and Other Commodities.bioRxiv : the preprint server for biology · 2025Article
- Computational mechanisms underlying multi-step planning deficits in methamphetamine use disorder.Translational psychiatry · 2025Article
- Explaining "U-shaped" distributions of population substance use with a simple computational approach.Journal of artificial societies and social simulation : JASSS · 2025Article
- Impact of provoked stress on model-free and model-based reinforcement learning in individuals with alcohol use disorder.Addictive behaviors reports · 2024Article
- Neuroimaging Biomarkers in Addiction.medRxiv : the preprint server for health sciences · 2024Article
- Aberrant neural computation of social controllability in nicotine-dependent humans.Communications biology · 2024Article
- Higher substance use is associated with low executive control neural activity and higher inflammation.Brain, behavior, and immunity · 2024Article
- Computational Mechanisms Underlying Multi-Step Planning Deficits in Methamphetamine Use Disorder.medRxiv : the preprint server for health sciences · 2024Article
- Neuromarkers in addiction: definitions, development strategies, and recent advances.Journal of neural transmission (Vienna, Austria : 1996) · 2024Review
- The utility of a latent-cause framework for understanding addiction phenomena.Addiction neuroscience · 2024Article
- Aberrant neural computation of social controllability in nicotine-dependent humans.Research square · 2024Article
- Model-based and model-free mechanisms in methamphetamine use disorder.Addiction biology · 2024Article
- Article
- Listening to the Data: Computational Approaches to Addiction and Learning.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Review
- Active learning impairments in substance use disorders when resolving the explore-exploit dilemma: A replication and extension of previous computational modeling results.Drug and alcohol dependence · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
In this brief review, we describe current computational models of drug-use and addiction that fall into 2 broad categories: mathematically based models that rely on computational theories, and brain-based models that link computations to brain areas or circuits. Across categories, many are models of learning and decision-making, which may be compromised in addiction. Several mathematical models take predictive coding approaches, focusing on Bayesian prediction error. Other models focus on learning processes and (traditional) prediction error. Brain-based models have incorporated prefrontal cortex, basal ganglia, and the dopamine system, based on the effects of drugs on dopamine, motivation, and executive control circuits. Several models specifically describe how behavioral control may transition from habitual to goal-directed systems, consistent with computational accounts of compromised "model-based" control. Some brain-based models have linked this to the transition of behavioral control from ventral to dorsal striatum. Overall, we propose that while computational models capture some aspects of addiction and have advanced our thinking, most have focused on the effects of drug use rather than addiction per se, most have not been tested on and/or supported by human data, and few capture multiple stages and symptoms of addiction. We conclude by suggesting a path forward for computational models of addiction. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.