Quantifying value-based determinants of drug and non-drug decision dynamics

A growing body of research suggests that substance use disorder (SUD) may be characterized as disorders of decision making. However, drug choice studies assessing drug-associated decision making often lack more complex and dynamic conditions that better approximate contexts outside the laboratory and may lead to incomplete conclusions regarding the nature of drug-associated value.

The current study assessed isomorphic (choice between identical food options) and allomorphic (choice between remifentanil [REMI] and food) choice across dynamically changing reward probabilities, magnitudes, and differentially reward-predictive stimuli in male rats to better understand determinants of drug value. Choice data were analyzed at aggregate and choice-by-choice levels using quantitative matching and reinforcement learning (RL) models, respectively.

Reductions in reward probability or magnitude independently reduced preferences for food and REMI commodities. Inclusion of reward-predictive cues significantly increased preference for food and REMI rewards. Model comparisons revealed that reward-predictive stimuli significantly altered the economic substitutability of food and REMI rewards at both levels of analysis. Furthermore, model comparisons supported the reformulation of reward value updating in RL models from independent terms to a shared, relative term, more akin to matching models.

The results indicate that value-based quantitative choice models can accurately capture choice determinants within complex decision-making contexts and corroborate drug choice as a multidimensional valuation process. Collectively, the present study indicates commonalities in decision-making for drug and non-drug rewards, validates the use of economic-based SUD therapies (e.g., contingency management), and implicates the neurobehavioral processes underlying drug-associated decision-making as a potential avenue for future SUD treatment.