Computational modelling of reinforcement learning and functional neuroimaging of probabilistic reversal for dissociating compulsive behaviours in gambling and cocaine use disorders.
Background: Individuals with cocaine use disorder or gambling disorder demonstrate impairments in cognitive flexibility: the ability to adapt to changes in the environment. Flexibility is commonly assessed in a laboratory setting using probabilistic reversal learning, which involves reinforcement le...
| Autores: | , , , , , , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:2445/208507 |
| Acceso en línea: | https://hdl.handle.net/2445/208507 |
| Access Level: | acceso abierto |
| Palabra clave: | Teoria de la predicció Cocaïna Joc compulsiu Prediction theory Cocaine Compulsive gambling |
| Sumario: | Background: Individuals with cocaine use disorder or gambling disorder demonstrate impairments in cognitive flexibility: the ability to adapt to changes in the environment. Flexibility is commonly assessed in a laboratory setting using probabilistic reversal learning, which involves reinforcement learning, the process by which feedback from the environment is used to adjust behavior. Aims: It is poorly understood whether impairments in flexibility differ between individuals with cocaine use and gambling disorders, and how this is instantiated by the brain. We applied computational modelling methods to gain a deeper mechanistic explanation of the latent processes underlying cognitive flexibility across two disorders of compulsivity. Method: We present a re-analysis of probabilistic reversal data from individuals with either gambling disorder (n = 18) or cocaine use disorder (n = 20) and control participants (n = 18), using a hierarchical Bayesian approach. Furthermore, we relate behavioural findings to their underlying neural substrates through an analysis of task-based functional magnetic resonanceimaging (fMRI) data. Results: We observed lower 'stimulus stickiness' in gambling disorder, and report differences in tracking expected values in individuals with gambling disorder compared to controls, with greater activity during reward expected value tracking in the cingulate gyrus and amygdala. In cocaine use disorder, we observed lower responses to positive punishment prediction errors and greater activity following negative punishment prediction errors in the superior frontal gyrus compared to controls. Conclusions: Using a computational approach, we show that individuals with gambling disorder and cocaine use disorder differed in their perseverative tendencies and in how they tracked value neurally, which has implications for psychiatric classification. |
|---|