Evidence map›Paper›PMID 25869543›Full record

ArticleDrug and alcohol dependence2015

Impaired Bayesian learning for cognitive control in cocaine dependence.

Jaime S Ide, Sien Hu, Sheng Zhang, Angela J Yu, Chiang-shan R Li

Open access · greenAbstract read
In one paragraph

Article in Drug and alcohol dependence, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 pooled it
2.1field-weighted citation impact, top 13% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.

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  9. Emotional Regulation in Young Adults with Internet Gaming Disorder.International journal of environmental research and public health · 2017
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 1 country.

Jaime S IdeDepartment of Psychiatry, Yale University School of Medicine, New Haven, CT 06519, USA; Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794, USA.
Sien HuDepartment of Psychiatry, Yale University School of Medicine, New Haven, CT 06519, USA.
Sheng ZhangDepartment of Psychiatry, Yale University School of Medicine, New Haven, CT 06519, USA.
Angela J YuDepartment of Cognitive Science, University of California, San Diego , La Jolla, CA 92093, USA.
Chiang-shan R LiDepartment of Psychiatry, Yale University School of Medicine, New Haven, CT 06519, USA; Department of Neurobiology, Yale University School of Medicine, New Haven, CT 06520, USA. Electronic address: chiang-shan.li@yale.edu.
Yale University · USUniversity of California, San Diego · US

Funding

Cognitive control and cocaine dependence: thalamic noradrenergic processesR01DA023248 · NIDA · YALE UNIVERSITY · PI LI, CHIANG-SHAN RAY · 2008 to 2018
$4.0M
Cerebral Correlates of Early Habitual DrinkingR01AA021449 · NIAAA · YALE UNIVERSITY · PI LI, CHIANG-SHAN RAY · 2013 to 2017
$1.2M
Imaging Cognitive Control in Cocaine DependenceK02DA026990 · NIDA · YALE UNIVERSITY · PI LI, CHIANG-SHAN RAY · 2009 to 2013
$639k
Amygdala Processes and Early Habitual DrinkingR21AA018004 · NIAAA · YALE UNIVERSITY · PI LI, CHIANG-SHAN RAY · 2009 to 2010
$383k
Thalamic Processes of Self Control in Cocaine DependenceR21DA026998 · NIDA · YALE UNIVERSITY · PI LI, CHIANG-SHAN RAY · 2009 to 2010
$373k
NIAAA NIH HHS R01 AA021449NIAAA NIH HHS R21 AA018004NIDA NIH HHS K02 DA026990NIDA NIH HHS K02DA026990NIDA NIH HHS R01 DA023248NIDA NIH HHS R01DA023248NIDA NIH HHS R21 DA026998NIDA NIH HHS R21DA026998
6 · The paper itself

Abstract

backgroundCocaine dependence is associated with cognitive control deficits. Here, we apply a Bayesian model of stop-signal task (SST) performance to further characterize these deficits in a theory-driven framework.

methodsA "sequential effect" is commonly observed in SST: encounters with a stop trial tend to prolong reaction time (RT) on subsequent go trials. The Bayesian model accounts for this by assuming that each stop/go trial increases/decreases the subject's belief about the likelihood of encountering a subsequent stop trial, P(stop), and that P(stop) strategically modulates RT accordingly. Parameters of the model were individually fit, and compared between cocaine-dependent (CD, n = 51) and healthy control (HC, n = 57) groups, matched in age and gender and both demonstrating a significant sequential effect (p < 0.05). Model-free measures of sequential effect, post-error slowing (PES) and post-stop slowing (PSS), were also compared across groups.

resultsBy comparing individually fit Bayesian model parameters, CD were found to utilize a smaller time window of past experiences to anticipate P(stop) (p < 0.003), as well as showing less behavioral adjustment in response to P(stop) (p < 0.015). PES (p = 0.19) and PSS (p = 0.14) did not show group differences and were less correlated with the Bayesian account of sequential effect in CD than in HC.

conclusionsCocaine dependence is associated with the utilization of less contextual information to anticipate future events and decreased behavioral adaptation in response to changes in such anticipation. These findings constitute a novel contribution by providing a computationally more refined and statistically more sensitive account of altered cognitive control in cocaine addiction.

Indexed as

CognitionLearningAdultAlcoholismAlgorithmsBayes TheoremCocaine-Related DisordersEducational StatusFemaleHumansMaleProspective StudiesReaction TimeBayesian modelingCocaine addictionCognitive controlConflict monitoringPost-error slowingSequential effect

Identifiers

PMID25869543
PMCPMC4447553
OpenAlexW2019755953

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

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.