title: 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 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.
authors.affiliation: Department of Veterans Affairs, New Jersey Health Care System, East Orange, NJ, USA; Department of Pharmacology, Physiology & Neuroscience, New Jersey Medical School, Rutgers, The State University of New Jersey, Newark, NJ, USA. Electronic address: Catherine.Myers2@va.gov.
authors.forename: Catherine E
authors.initials: CE
authors.lastname: Myers
authors.affiliation: Veterans Affairs Ann Arbor Healthcare System, Ann Arbor, MI, USA; Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA.
authors.forename: Jony
authors.initials: J
authors.lastname: Sheynin
authors.affiliation: School of Social Sciences and Psychology, University of Western Sydney, Sydney, NSW, Australia.
authors.forename: Tarryn
authors.initials: T
authors.lastname: Balsdon
authors.affiliation: School of Mathematics, Computing Sciences & Engineering at City University London, UK.
authors.forename: Andre
authors.initials: A
authors.lastname: Luzardo
authors.affiliation: Department of Veterans Affairs, New Jersey Health Care System, East Orange, NJ, USA; Department of Pharmacology, Physiology & Neuroscience, New Jersey Medical School, Rutgers, The State University of New Jersey, Newark, NJ, USA.
authors.forename: Kevin D
authors.initials: KD
authors.lastname: Beck
authors.affiliation: School of Psychology, University of New South Wales, Sydney, NSW, Australia; School of Psychology, University of Exeter, Exeter, UK.
authors.forename: Lee
authors.initials: L
authors.lastname: Hogarth
authors.affiliation: Drug Health Services, Addiction Medicine, Central Clinical School, Royal Prince Alfred Hospital, The University of Sydney, Sydney, NSW, Australia.
authors.forename: Paul
authors.initials: P
authors.lastname: Haber
authors.affiliation: School of Social Sciences and Psychology, University of Western Sydney, Sydney, NSW, Australia; Marcs Institute for Brain and Behaviour, University of Western Sydney, Sydney, NSW, Australia. Electronic address: a.moustafa@uws.edu.au.
authors.forename: Ahmed A
authors.initials: AA
authors.lastname: Moustafa
mesh_terms: D000328:Adult; D003657:Decision Making / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009293:Opioid-Related Disorders / Q000503:physiopathology*; D011337:Probability Learning; D011678:Punishment; D012201:Reward
keywords: Addiction; Punishment learning; Reward learning