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

Abstract

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 thatopioid-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.

Learning and generalization from reward and punishment in opioid addiction

“…As illustrated in Figure 3B, the opposite effect was obtained: specifically, while the opioid-addicted and control groups were comparable on reward-based learning, the addicted group performed worse than controls on punishment-based learning. This in turn suggests that the increased lose-shift behavior of opioid-addicted participants, previously observed by Myers et al (11), did not simply reflect superior learning to adjust responding after negative outcomes, but rather reflected impairment in the ability to “stick with” successful strategies to maximize performance in the face of unexpected negative outcomes during a probabilistic task.…”

“…In a prior study using a probabilistic reward- and punishment-learning task, Myers et al (11) found that opioid-addicted individuals were more prone to show lose-shift behavior after an unexpected negative outcome; one interpretation of that result might be that the addicted patients were simply better at learning to avoid punishment. One aim of the current study was to test that idea, by comparing opioid-addicted and control groups on a deterministic version of the reward- and punishment-learning task, where both rewarding and punishing outcomes are perfectly predictable.…”

“…An important feature of the current task, as well as of the prior probabilistic reward- and punishment-learning task studied by Myers et al (11), is the presence of an ambiguous, no-feedback outcome that can signal either missed opportunity for reward or successful avoidance of punishment. Thus, successful performance on punishment-based trials in the current task involves learning that the no-feedback outcome is reinforcing.…”

“…In a prior study, Myers et al (11) used a categorization task that interleaved trials on which participants learned to avoid negative consequences (point loss) with trials on which they learned to obtain positive consequences (point gain). There was also an ambiguous “no-feedback” outcome, which could signal either missed opportunity for reward or else successful avoidance of punishment.…”

Differential sensitivity to learning from positive and negative outcomes in cocaine users

“…The feedback-based probabilistic learning task has previously been used to evaluate changes in learning that may underlie degenerative and psychiatric conditions, including Parkinson’s disease, post-traumatic stress disorder, schizophrenia, opioid use disorder and major depressive disorder (Bodi et al, 2009; Herzallah et al, 2013; Myers et al, 2013, 2016; Somlai et al, 2011). Non-medicated Parkinson’s patients show a decreased sensitivity to positive, but not negative, outcomes relative to controls (Bodi et al, 2009; Piray et al, 2014).…”