Probabilistic reward- and punishment-based learning in opioid addiction: Experimental and computational data

Catherine E. Myers, Jony Sheynin, Tarryn Balsdon, Andre Luzardo, Kevin D. Beck, Lee Hogarth, Paul Haber, Ahmed A. Moustafa*

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

42 Citations (Scopus)


Addiction is the continuation of a habit in spite of negative consequences. A vast literature gives evidence that this poor decision-making behavior in individuals addicted to drugs also generalizes to laboratory decision making tasks, suggesting that the impairment in decision-making is not limited to decisions about taking drugs. In the current experiment, opioid-addicted individuals and matched controls with no history of illicit drug use were administered a probabilistic classification task that embeds both reward-based and punishment-based learning trials, and a computational model of decision making was applied to understand the mechanisms describing individuals' performance on the task. Although behavioral results showed that opioid-addicted individuals performed as well as controls on both reward- and punishment-based learning, the modeling results suggested subtle differences in how decisions were made between the two groups. Specifically, the opioid-addicted group showed decreased tendency to repeat prior responses, meaning that they were more likely to "chase reward" when expectancies were violated, whereas controls were more likely to stick with a previously-successful response rule, despite occasional expectancy violations. This tendency to chase short-term reward, potentially at the expense of developing rules that maximize reward over the long term, may be a contributing factor to opioid addiction. Further work is indicated to better understand whether this tendency arises as a result of brain changes in the wake of continued opioid use/abuse, or might be a pre-existing factor that may contribute to risk for addiction.

Original languageEnglish
Pages (from-to)240-248
Number of pages9
JournalBehavioural Brain Research
Publication statusPublished - 1 Jan 2016
Externally publishedYes


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