Introduction: People with addiction will continue to use drugs despite adverse long-term consequences. We hypothesized (a) that this deficit persists during substitution treatment, and (b) that this deficit might be related not only to a desire for immediate gratification, but also to a lower sensitivity for optimal decision making. We investigated how individuals with a history of heroin addiction perform (compared to healthy controls) in a virtual reality delay discounting task. This novel task adds to established measures of delay discounting an assessment of the optimality of decisions, especially in how far decisions are influenced by a general choice bias and/or a reduced sensitivity to the relative value of the two alternative rewards. We used this measure of optimality to apply diffusion model analysis to the behavioral data to analyze the interaction between decision optimality and reaction time. The addiction group consisted of 25 patients with a history of heroin dependency currently participating in a methadone maintenance program; the control group consisted of 25 healthy participants with no history of substance abuse, who were recruited from the Western Sydney community.
Results: The patient group demonstrated greater levels of delay discounting compared to the control group, which is broadly in line with previous observations. Diffusion model analysis yielded a reduced sensitivity for the optimality of a decision in the patient group compared to the control group. This reduced sensitivity was reflected in lower rates of information accumulation and higher decision criteria.
Conclusions: Increased discounting in individuals with heroin addiction is related not only to a generally increased bias to immediate gratification, but also to reduced sensitivity for the optimality of a decision. This finding is in line with other findings about the sensitivity of addicts in distinguishing optimal from nonoptimal choice options.
|Number of pages||12|
|Journal||Journal of Clinical and Experimental Neuropsychology|
|Publication status||Published - 7 Feb 2018|