Excessive state switching underlies reversal learning deficits in cocaine users

Edward H. Patzelt, Zeb Kurth-Nelson, Kelvin O. Lim, Angus W. MacDonald

Research output: Contribution to journalArticle

7 Scopus citations


Background: Markers of chronic cocaine exposure on neural mechanisms in animals and humans is of great interest. The probabilistic reversal-learning task may be an effective way to examine dysfunction associated with cocaine addiction. However the exact nature of the performance deficits observed in cocaine users has yet to be disambiguated. Method: Data from a probabilistic reversal-learning task performed by 45 cocaine users and 41 controls was compared and fit to a Bayesian hidden Markov model (HMM). Results: Cocaine users demonstrated the predicted performance deficit in achieving the reversal criterion relative to controls. The deficit appeared to be due to excessive switching behavior as evidenced by responsivity to false feedback and spontaneous switching. This decision-making behavior could be captured by a single parameter in an HMM and did not require an additional parameter to represent perseverative errors. Conclusions: Cocaine users are characterized by excessive switching behavior on the reversal-learning task. While there may be a compulsive component to behavior on this task, impulsive decision-making may be more relevant to observed impairment. This is important in building diagnostic tools to quantify the degree to which each type of dysfunction is present in individuals, and may play a role in developing treatments for those dysfunctions.

Original languageEnglish (US)
Pages (from-to)211-217
Number of pages7
JournalDrug and alcohol dependence
Issue number1
StatePublished - Jan 1 2014


  • Bayesian hidden Markov model
  • Cocaine
  • Decision making
  • Impulsivity
  • Reversal learning
  • State switching

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