Behavioral and computational signatures of reinforcement learning and confidence biases in gambling disorder

Gambling Disorder (GD) is associated with maladaptive decision-making, possibly driven by biases in learning and confidence judgments. While prior research report abnormal learning rates and heightened overconfidence in GD, the affected cognitive mechanism producing these joint deficits has so far remained unidentified. Our study aims to fill this gap using a recently established reinforcement learning (RL) experimental and computational framework linking learning processes, outcome-valence effects and confidence judgments.

We pre-registered and tested the hypotheses that GD patients exhibit increased (over)confidence and confirmatory learning bias, and increased outcome valence effects on choice accuracy and confidence judgements in in 18 participants with GD and 19 matched controls.

While our findings replicated the main behavioral patterns of choices and confidence judgments, and confirmed their computational foundations, we did not find any group differences between the controls and patients with GD.

The current findings speak to the inconsistent findings of abnormalities in confidence and learning in GD. Systematic research is necessary to better understand the influence of possibly mediating factors such as disorder-related idiosyncrasies (e.g. skill- vs chance-based preferences) to further clarify if, when and how confidence and learning are affected in people with GD.