ReviewThe Journal of neuroscience : the official journal of the Society for Neuroscience2023
Listening to the Data: Computational Approaches to Addiction and Learning.
Review in The Journal of neuroscience : the official journal of the Society for Neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Neural Reward Processing in Digital Addiction: A Dynamical Systems Theory of Reward Instability.Brain sciences · 2026Article
- Model-based analysis of stop-signal data reveals robust neural and clinical correlates of evidence accumulation but not inhibition.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Article
- Diminishing loss sensitivity during risky decision-making among male individuals with gambling disorder.Journal of behavioral addictions · 2026Article
- Sex-specific effects of predator scent stress on fear, anxiety-like behavior and methamphetamine seeking in rats.Addiction neuroscience · 2025Article
- Longitudinal changes in reinforcement learning during smoking cessation: a computational analysis using a probabilistic reward task.Scientific reports · 2024Article
- Dynamic overrepresentation of accumbal cues in food- and opioid-seeking rats after prenatal THC exposure.Science advances · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Computational approaches hold great promise for identifying novel treatment targets and creating translational therapeutics for substance use disorders. From circuitries underlying decision-making to computationally derived neural markers of drug-cue reactivity, this review is a summary of the approaches to data presented at our 2023 Society for Neuroscience Mini-Symposium. Here, we highlight data- and hypothesis-driven computational approaches that recently afforded advancements in addiction and learning neuroscience. First, we discuss the value of hypothesis-driven algorithmic modeling approaches, which integrate behavioral, neural, and cognitive outputs to refine hypothesis testing. Then, we review the advantages of data-driven dimensionality reduction and machine learning methods for uncovering novel predictor variables and elucidating relationships in high-dimensional data. Overall, this review highlights recent breakthroughs in cognitive mapping, model-based analysis of behavior/risky decision-making, patterns of drug taking, relapse, and neuromarker discovery, and showcases the benefits of novel modeling techniques, across both preclinical and clinical data.
Indexed as
Identifiers
What OpenQuestion holds
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.