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Drug Alcohol Depend. 2015 Jun 1;151:220-7. doi: 10.1016/j.drugalcdep.2015.03.021. Epub 2015 Apr 2.

Impaired Bayesian learning for cognitive control in cocaine dependence.

Author information

1
Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06519, USA; Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794, USA.
2
Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06519, USA.
3
Department of Cognitive Science, University of California, San Diego , La Jolla, CA 92093, USA.
4
Department 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.

Abstract

BACKGROUND:

Cocaine 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.

METHODS:

A "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.

RESULTS:

By 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.

CONCLUSIONS:

Cocaine 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.

KEYWORDS:

Bayesian modeling; Cocaine addiction; Cognitive control; Conflict monitoring; Post-error slowing; Sequential effect

PMID:
25869543
PMCID:
PMC4447553
DOI:
10.1016/j.drugalcdep.2015.03.021
[Indexed for MEDLINE]
Free PMC Article

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