Gambling Behavior and Treatments

Computational modeling of reinforcement learning and functional neuroimaging of probabilistic reversal dissociates compulsive behaviors in Gambling and Cocaine Use Disorders

The paper uses reinforcement learning models and fMRI to analyze cognitive flexibility differences in Gambling Disorder and Cocaine Use Disorder, finding distinct neural and behavioral patterns.

Striatal ups or downs? Neural correlates of monetary reward anticipation, cue reactivity and their interaction in alcohol use disorder and gambling disorder

This study investigates striatal activation during reward anticipation with and without addiction-related cues in alcohol use disorder and gambling disorder, finding heterogeneous striatal dysfunction and cue-related behavioral interactions.

The modulation of acute stress on Model-Free and Model-Based reinforcement learning in Gambling Disorder

This study examines how acute stress affects the balance between model-based and model-free reinforcement learning in pathological gamblers and healthy controls, finding stress impacts this balance more in controls than gamblers.

Metacognition and the effect of incentive motivation in two compulsive disorders: gambling disorder and obsessive-compulsive disorder

This study investigates metacognition and incentive motivation in gambling disorder and obsessive-compulsive disorder using fMRI, finding increased confidence in GD during gain contexts but no group differences in VMPFC activity.

Gambling environment exposure increases temporal discounting but improves model-based control in regular slot-machine gamblers.

Attenuated directed exploration during reinforcement learning in gambling disorder

This study finds that gamblers show reduced strategic exploration during reinforcement learning, linked to altered fronto-parietal network activity and potential dopamine system changes.

Neural correlates of cue-induced changes in decision-making distinguish subjects with gambling disorder from healthy controls

This study identifies neural signatures of cue-induced decision-making differences between gambling disorder and healthy controls using fMRI and machine learning.

Cue-induced effects on decision-making distinguish subjects with gambling disorder from healthy controls

This study uses machine learning to show that cue-induced changes in gamble acceptance distinguish gambling disorder from healthy controls, with loss aversion not contributing to classification.

Altered orbitofrontal sulcogyral patterns in gambling disorder: a multicenter study

This study finds a skewed distribution of orbitofrontal sulcogyral patterns in gambling disorder, with increased prevalence of Type II patterns compared to healthy controls.