Impaired Bayesian learning for cognitive control in cocaine dependence. (1st June 2015)

• Record Type: Journal Article
• Authors: Ide, Jaime S.; Hu, Sien; Zhang, Sheng; Yu, Angela J.; Li, Chiang-shan R.
• Is Part Of: Drug and alcohol dependence. Volume 151(2015)
• Page Start: 220
• Page End: 227
• Publication Date: 2015-06-01
• Subjects: Cocaine addiction -- Cognitive control -- Bayesian modeling -- Sequential effect -- Conflict monitoring -- Post-error slowing; Drug abuse -- Periodicals; Alcoholism -- Periodicals; 616.86
• DOI: 10.1016/j.drugalcdep.2015.03.021
• Languages: English
• ISSNs: 0376-8716

Abstract

Highlights: We used a Bayesian model to examine cognitive control in cocaine dependence. Addicts showed less sequential effect and slower learning rate, compared to HC. Results suggest deficient utilization of contextual information in addicts. Findings provide a new account of altered cognitive control in cocaine addiction. 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.