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Sequential gambling task revealed altered impulse control and reduced risk sensitivity in patients with gambling disorder, indicating significant behavioral modulation issues.
BACKGROUND: Gambling disorder (GD) involves persistent risky choices despite losses, suggesting impaired impulse control. While static paradigms reveal inhibition deficits in GD, they cannot model dynamic risk-reward escalations during real gambling. This study aims to investigate whether GD involves impaired dynamic impulse control during escalating stakes and to dissociate contributions of subjective risk evaluation and trait impulsivity to this deficit. METHODS: Using a sequential gambling task with 83 male patients with GD and 62 matched healthy controls (HCs), this study investigated dynamic impulse control deficits under escalating stakes. We quantified dynamic impulse control via the reward-reaction time (RT) coupling for 'continue' choices (dynamic impulse control index [DICI]) using Bayesian modeling. Risk sensitivity and risk preference were derived from stop/continue decisions. Trait impulsivity was assessed with the Barratt Impulsiveness Scale (BIS-11). Regression analyses examined the modulation of DICI by risk sensitivity and trait impulsivity. RESULTS: Patients with GD exhibited significantly attenuated DICI versus HCs, reflecting failure to increase deliberation with escalating stakes. Computational modeling revealed markedly reduced risk sensitivity in GD despite comparable risk preference. Critically, trait impulsivity positively modulated DICI in HCs but not in GD, indicating pathological decoupling. Risk sensitivity positively predicted DICI in both groups, though significantly weaker in GD. CONCLUSIONS: These findings establish a triadic impairment in GD: (1) attenuated adaptive impulse control during escalation (impaired DICI), (2) deficient subjective risk weighting (reduced sensitivity), and (3) breakdown of impulsivity-based modulation of control. This reveals a dynamic, mechanism-focused pathology beyond static trait models.
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Zhong et al. (2026) studied this question.
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