Theory-driven computational models of drug addiction in humans: Fruitful or futile?

Abstract

Maladaptive behavior in drug addiction is widely regarded as a result of neurocognitive dysfunctions. Recently, there has been a growing trend to adopt computational methods to study these dysfunctions in drug-addicted patients, not least because it provides a quantitative framework to infer the psychological mechanisms that may have gone awry in addiction. We therefore sought to evaluate the extent to which these theory-driven computational models have fulfilled this purpose in addiction research. We discuss several learning and decision-making theories proposed to explain symptoms that characterize impaired control and the intense urge to use drugs in addiction, and outline the computational algorithms frequently used to model these processes. Specifically, impaired behavioral control over drugs have been explained by aberrant reinforcement learning algorithms and an imbalance between model-based and model-free control, whereas the strong desire for drugs might be explained by a neurocomputational model of incentive sensitization and behavioral economic theory. We argue that while theory-driven computational models may appear to be useful tools that generate novel mechanistic insights into drug addiction, their use should be informed by psychological theory, experimental data, and clinical observations.

Keywords

• Habit learning
• Reinforcement learning
• Substance use disorder
• Computational psychiatry
• Drugs of abuse
• Cognitive modeling