Impaired Bayesian learning for cognitive control in cocaine dependence.

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.