Computational Mechanisms of Addiction: Recent Evidence and Its Relevance to Addiction Medicine

A decade ago, we hypothesized that drug addiction can be viewed as a transition from voluntary, recreational drug use to compulsive drug-seeking habits, neurally underpinned by a transition from prefrontal cortical to striatal control over drug seeking and taking as well as a progression from the ventral to the dorsal striatum. Here, in the light of burgeoning, supportive evidence, we reconsider and elaborate this hypothesis, in particular the refinements in our understanding of ventral and dorsal striatal mechanisms underlying goal-directed and habitual drug seeking, the influence of drug-associated Pavlovian-conditioned stimuli on drug seeking and relapse, and evidence for impairments in top-down prefrontal cortical inhibitory control over this behavior. We further review animal and human studies that have begun to define etiological factors and individual differences in the propensity to become addicted to drugs, leading to the description of addiction endophenotypes, especially for cocaine addiction. We consider the prospect of novel treatments for addiction that promote abstinence from and relapse to drug use.

Drug addiction is increasingly viewed as the endpoint of a series of transitions from initial drug use--when a drug is voluntarily taken because it has reinforcing, often hedonic, effects--through loss of control over this behavior, such that it becomes habitual and ultimately compulsive. Here we discuss evidence that these transitions depend on interactions between pavlovian and instrumental learning processes. We hypothesize that the change from voluntary drug use to more habitual and compulsive drug use represents a transition at the neural level from prefrontal cortical to striatal control over drug seeking and drug taking behavior as well as a progression from ventral to more dorsal domains of the striatum, involving its dopaminergic innervation. These neural transitions may themselves depend on the neuroplasticity in both cortical and striatal structures that is induced by chronic self-administration of drugs.

To use sensory information efficiently to make judgments and guide action in the world, the brain must represent and use information about uncertainty in its computations for perception and action. Bayesian methods have proven successful in building computational theories for perception and sensorimotor control, and psychophysics is providing a growing body of evidence that human perceptual computations are "Bayes' optimal". This leads to the "Bayesian coding hypothesis": that the brain represents sensory information probabilistically, in the form of probability distributions. Several computational schemes have recently been proposed for how this might be achieved in populations of neurons. Neurophysiological data on the hypothesis, however, is almost non-existent. A major challenge for neuroscientists is to test these ideas experimentally, and so determine whether and how neurons code information about sensory uncertainty.