Recent Opioid Use Impedes Range Adaptation in Reinforcement Learning in Human Addiction

Background: Drugs like opioids are potent reinforcers thought to co-opt value-based decisions by overshadowing other rewarding outcomes, but how this happens at a neurocomputational level remains elusive. Range adaptation is a canonical process of fine-tuning representations of value based on reward context. Here, we tested whether recent opioid exposure impacts range adaptation in opioid use disorder, potentially explaining why shifting decision making away from drug taking during this vulnerable period is so difficult.

Methods: Participants who had recently (<90 days) used opioids (n = 34) or who had abstained from opioid use for ≥ 90 days (n = 20) and comparison control participants (n = 44) completed a reinforcement learning task designed to induce robust contextual modulation of value. Two models were used to assess the latent process that participants engaged while making their decisions: 1) a Range model that dynamically tracks context and 2) a standard Absolute model that assumes stationary, objective encoding of value.

Results: Control participants and ≥90-days-abstinent participants with opioid use disorder exhibited choice patterns consistent with range-adapted valuation. In contrast, participants with recent opioid use were more prone to learn and encode value on an absolute scale. Computational modeling confirmed the behavior of most control participants and ≥90-days-abstinent participants with opioid use disorder (75%), but a minority in the recent use group (38%), was better fit by the Range model than the Absolute model. Furthermore, the degree to which participants relied on range adaptation correlated with duration of continuous abstinence and subjective craving/withdrawal.

Conclusions: Reduced context adaptation to available rewards could explain difficulty deciding about smaller (typically nondrug) rewards in the aftermath of drug exposure.