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Impaired Bayesian learning for cognitive control in cocaine dependence.
Jaime S Ide1, Sien Hu2, Sheng Zhang2
1Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06519, USA; Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794, USA.
Drug and Alcohol Dependence
|April 15, 2015
Summary
Cocaine dependence impairs cognitive control by reducing the use of past experiences to predict future events and adapt behavior. This study used a Bayesian model to reveal these deficits in cocaine-dependent individuals.
Area of Science:
- Neuroscience
- Cognitive Psychology
- Computational Psychiatry
Background:
- Cocaine dependence is linked to cognitive control impairments.
- Understanding these deficits is crucial for addiction treatment.
Purpose of the Study:
- To characterize cognitive control deficits in cocaine dependence using a Bayesian model of stop-signal task (SST) performance.
- To investigate how cocaine-dependent individuals update their beliefs about upcoming stop signals and adjust their behavior accordingly.
Main Methods:
- Applied a Bayesian computational model to analyze stop-signal task (SST) performance.
- Compared model parameters between cocaine-dependent (CD) and healthy control (HC) groups.
- Assessed model-free measures like post-error slowing (PES) and post-stop slowing (PSS).
Main Results:
- Cocaine-dependent individuals used a smaller time window to predict stop-signal probability (P(stop)).
- CD groups exhibited less behavioral adjustment in response to P(stop) compared to HC.
- Model-free measures (PES, PSS) did not differ between groups and showed weaker correlation with the Bayesian model in CD.
Conclusions:
- Cocaine dependence is associated with reduced use of contextual information for anticipation.
- Individuals with cocaine dependence show decreased behavioral adaptation to anticipated changes.
- This study offers a refined computational account of altered cognitive control in cocaine addiction.
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