Appetitive Motivation and Regulatory Processes in Adolescent Ketamine Users

Abstract

Background

Ketamine has remained the most commonly used illicit drug among adolescents in Taiwan. A dual process model proposes that addictive behaviors develop in adolescents as a result of an imbalance between an appetitive, approach-oriented system (implicit and explicit attitudes) and a regulatory executive system (cool and hot executive functions). We aimed to examine the appetitive and regulatory processes in adolescent ketamine users in comparison to matched healthy adolescents. Method: The participants were 30 adolescent ketamine users and 32 nondrug controls, matched with gender, age, education years, and education systems. Both groups completed the affective priming task (APT), the stop-signal task (SST), an Iowa Gambling Task (IGT), and finally a Drug Use Disorders Identification Test: Extended (DUDIT-E). Results: The controls had higher positive and negative outcome expectancy with respect to using ketamine compared to the adolescent ketamine users. There was no significant between-group performance difference in APT. The adolescent ketamine users may have shown marginally poorer performance compared to the controls in hot executive functions (IGT), but their cold executive functions (SST) remained intact. Conclusion: The current study reported that the adolescent ketamine users may not have imbalanced dual processes (biased appetitive motivation and impaired regulatory executive process). A different therapeutic focus on adolescent ketamine users may be developed accordingly. More advocacies on ketamine’s aversive outcomes are needed, particularly on campus in order to reduce substance misuse.

Keywords:

• Ketamine
• outcome expectancy
• implicit attitude
• executive function

Declaration of interest

The authors declare that they have no conflict of interest. The authors alone are responsible for the content and writing of the article.

Notes

1 We computed the BF₀₁, a ratio of the likelihood of the data fitting given the null hypothesis (H₀) (i.e., p(data| H₀)) relative to the data fitting given the research hypothesis (H₁) (i.e., p(data| H₁)) (van de Schoot & Depaoli, 2014). As the BF₀₁ increases (larger than 1), there is more evidence in favor of H₀ (e.g., 1–3 as weak evidence for H₀, 3–10 positive for H₀) (Jarosz & Wiley, 2014). Alternatively, as the BF₀₁ decreases (smaller than 1), there is more evidence supporting H₁ (e.g., 1–.33 as weak evidence for H₁, .33–.10 positive for H₁), ibid.