Computational models of drug use and addiction: A review.

Abstract

In this brief review, we describe current computational models of drug-use and addiction that fall into 2 broad categories: mathematically based models that rely on computational theories, and brain-based models that link computations to brain areas or circuits. Across categories, many are models of learning and decision-making, which may be compromised in addiction. Several mathematical models take predictive coding approaches, focusing on Bayesian prediction error. Other models focus on learning processes and (traditional) prediction error. Brain-based models have incorporated prefrontal cortex, basal ganglia, and the dopamine system, based on the effects of drugs on dopamine, motivation, and executive control circuits. Several models specifically describe how behavioral control may transition from habitual to goal-directed systems, consistent with computational accounts of compromised "modelbased" control. Some brain-based models have linked this to the transition of behavioral control from ventral to dorsal striatum. Overall, we propose that while computational models capture some aspects of addiction and have advanced our thinking, most have focused on the effects of drug use rather than addiction per se, most have not been tested on and/or supported by human data, and few capture multiple stages and symptoms of addiction. We conclude by suggesting a path forward for computational models of addiction.

A review of mathematical modeling of addiction regarding both (neuro-)psychological processes and the social contagion perspectives

“…Finally, the tables include which type of mathematical framework is used, giving an indication of the type of the main variables and methods used. While we discuss some models that focus on the psychophysiological effects of drug intake, we refer to the recent work by Mollick and Kober (2020) for a more in-depth review of models of this type.…”

What we think about when we think about predictive processing.

“…Emotional states thus have been hypothesized to reflect changes in the uncertainty about the somatic consequences of action, such that increased precision of predictions about the (controllable) future results in positively valenced brain states while a loss of prior precision and uncertainty about the consequences of action are associated with negatively valenced states (Clark, Watson, & Friston, 2018; Joffily & Coricelli, 2013; Seth & Friston, 2016). Here, Mollick and Kober (2020) discuss models of drug addiction characterized by more precise beliefs about reward-related physiological states in addicted individuals, who then ignore sensory evidence to the contrary (e.g., inaccurate priors). More recent models have extended the idea of precise priors to a further level of the hierarchy and proposed that depressed or anxious mood acts as a hyperprior such that the brain is certain that it will encounter uncertain uncontrollable environments (Clark et al, 2018).…”

Can biophysical models give insight into the synaptic changes associated with addiction?

“…Although there are a repertoire of theoretical models of drug use and addiction, they rely on theories of how the brain solves computational, information-processing, and control problems (11 , 12 ). Some of these models link these computations with specific brain areas (12 ). However, to our knowledge, they do not consider the structural and molecular changes of the neurons and, instead, focus on drug use behavior.…”