Markov computational modeling to predict opioid vs. money choice and behavioral effort in regular heroin users

Quantifying decision-making in experimental settings that mimic real-world conditions may provide insights into mechanisms underlying addiction. This study developed a computational model of opioid-seeking behavior. Out-of-treatment persons who regularly used heroin were stabilized on buprenorphine 8mg/day to minimize opioid withdrawal. Across programmatically-linked studies, three experimental conditions presented differing money vs. opioid unit amounts that could be earned per trial ($2 vs. 1-mg hydromorphone, n=23; $2 vs. 2-mg hydromorphone, n=36; $4 vs. 2-mg hydromorphone, n=24), controlling other factors. Progressive ratio schedules on each choice option required increasing effort across trials to earn the same amount. Trial-level outcomes were decision latency and choice on each option, and session-level outcomes were drug-money latency and breakpoint difference scores. A Markov computational model was used to predict the probability of choosing the same option as the previous trial (vs. switching). Model inputs included effort discrepancy (between earning the same vs. other commodity on next choice) and logarithm of the ratio of decisional speed (current vs. previous choice). Participants who more rapidly chose hydromorphone vs. money made more consecutive drug choices and expended greater effort earning hydromorphone. First-trial hydromorphone choice predicted continued effortful opioid-seeking. Participants repeated choices on 80% of trials; the model accurately predicted stick vs. switch behavior on 93% of trials. Participants typically repeated choices when faced with lower effort discrepancies and higher hydromorphone dose (2-mg vs. 1-mg). In conclusion, a Markov computational model accurately predicted effortful behavior in a choice paradigm that mimics real-world decisions between opioid and nondrug reinforcers.