Trial reportBiological psychiatry. Cognitive neuroscience and neuroimaging2019
In Cocaine Dependence, Neural Prediction Errors During Loss Avoidance Are Increased With Cocaine Deprivation and Predict Drug Use.
Trial report in Biological psychiatry. Cognitive neuroscience and neuroimaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 23 citations in OpenAlex.
- Neurobehavioral differences in value-based decision making between people with cocaine use disorder and controls.Experimental and clinical psychopharmacology · 2026Article
- A computational mechanism linking momentary craving and decision-making in alcohol drinkers and cannabis users.Nature. Mental health · 2026Article
- Reinforcement-Learning-Informed Queries Guide Behavioral Change.Clinical psychological science : a journal of the Association for Psychological Science · 2024Article
- Discriminative functional connectivity signature of cocaine use disorder links to rTMS treatment response.Nature. Mental health · 2024Article
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- A generalizable functional connectivity signature characterizes brain dysfunction and links to rTMS treatment response in cocaine use disorder.medRxiv : the preprint server for health sciences · 2023Article
- A neuropeptide signal confers ethanol state dependency during olfactory learning inProceedings of the National Academy of Sciences of the United States of America · 2022Article
- Reinforcement learning detuned in addiction: integrative and translational approaches.Trends in neurosciences · 2022Review
- Computational analysis of probabilistic reversal learning deficits in male subjects with alcohol use disorder.Frontiers in psychiatry · 2022Article
- Computational approaches and machine learning for individual-level treatment predictions.Psychopharmacology · 2021Review
- Computational theory-driven studies of reinforcement learning and decision-making in addiction: What have we learned?Current opinion in behavioral sciences · 2021Article
- Neuroimaging reward, craving, learning, and cognitive control in substance use disorders: review and implications for treatment.The British journal of radiology · 2019Review
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundIn substance-dependent individuals, drug deprivation and drug use trigger divergent behavioral responses to environmental cues. These divergent responses are consonant with data showing that short- and long-term adaptations in dopamine signaling are similarly sensitive to state of drug use. The literature suggests a drug state-dependent role of learning in maintaining substance use; evidence linking dopamine to both reinforcement learning and addiction provides a framework to test this possibility.
methodsIn a randomized crossover design, 22 participants with current cocaine use disorder completed a probabilistic loss-learning task during functional magnetic resonance imaging while on and off cocaine (44 sessions). Another 54 participants without Axis I psychopathology served as a secondary reference group. Within-drug state and paired-subjects' learning effects were assessed with computational model-derived individual learning parameters. Model-based neuroimaging analyses evaluated effects of drug use state on neural learning signals. Relationships among model-derived behavioral learning rates (α+, α-), neural prediction error signals (δ+, δ-), cocaine use, and desire to use were assessed.
resultsDuring cocaine deprivation, cocaine-dependent individuals exhibited heightened positive learning rates (α+), heightened neural positive prediction error (δ+) responses, and heightened association of α+ with neural δ+ responses. The deprivation-enhanced neural learning signals were specific to successful loss avoidance, comparable to participants without psychiatric conditions, and mediated a relationship between chronicity of drug use and desire to use cocaine.
conclusionsNeurocomputational learning signals are sensitive to drug use status and suggest that heightened reinforcement by successful avoidance of negative outcomes may contribute to drug seeking during deprivation. More generally, attention to drug use state is important for delineating substrates of addiction.
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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.