Disorders of compulsivity: a common bias towards learning habits.

Why do we repeat choices that we know are bad for us? Decision making is characterized by the parallel engagement of two distinct systems, goal-directed and habitual, thought to arise from two computational learning mechanisms, model-based and model-free. The habitual system is a candidate source of pathological fixedness. Using a decision task that measures the contribution to learning of either mechanism, we show a bias towards model-free (habit) acquisition in disorders involving both natural (binge eating) and artificial (methamphetamine) rewards, and obsessive-compulsive disorder. This favoring of model-free learning may underlie the repetitive behaviors that ultimately dominate in these disorders. Further, we show that the habit formation bias is associated with lower gray matter volumes in caudate and medial orbitofrontal cortex. Our findings suggest that the dysfunction in a common neurocomputational mechanism may underlie diverse disorders involving compulsion.

A neurocognitive comparison of cognitive flexibility and response inhibition in gamblers with varying degrees of clinical severity.

BACKGROUND: As a behavioral addiction with clinical and phenomenological similarities to substance addiction, recreational and pathological gambling represent models for studying the neurobiology of addiction, without the confounding deleterious brain effects which may occur from chronic substance abuse. METHOD: A community sample of individuals aged 18-65 years who gamble was solicited through newspaper advertising. Subjects were grouped a priori into three groups (no-risk, at-risk, and pathological gamblers) based on a diagnostic interview. All subjects underwent a psychiatric clinical interview and neurocognitive tests assessing motor impulsivity and cognitive flexibility. Subjects with a current axis I disorder, history of brain injury/trauma, or implementation or dose changes of psychoactive medication within 6 weeks of study enrollment were excluded. RESULTS: A total of 135 no-risk, 69 at-risk and 46 pathological gambling subjects were assessed. Pathological gamblers were significantly older, and exhibited significant deficiencies in motor impulse control (stop-signal reaction times), response speed (median 'go' trial response latency) and cognitive flexibility [total intra-dimensional/extra-dimensional (IDED) errors] versus controls. The finding of impaired impulse control and cognitive flexibility was robust in an age-matched subgroup analysis of pathological gamblers. The no-risk and at-risk gambling groups did not significantly differ from each other on task performance. CONCLUSIONS: Impaired response inhibition and cognitive flexibility exist in people with pathological gambling compared with no-risk and at-risk gamblers. The early identification of such illness in adolescence or young adulthood may aid in the prevention of addiction onset of such disabling disorders.