Computational modelling of decision-making in suicide attempters

Abstract Importance Suicide is a leading cause of mortality, yet its prevention remains a challenge. Suicide attempters exhibit disrupted decision-making, but the specific cognitive processes driving this impairment and their utility for risk stratification are unclear. Objective To determine which computational decision-making mechanisms are disrupted in high-risk suicide attempters, and whether they distinguish attempters from depressed and healthy controls. Design This cross-sectional study fit reinforcement-learning models to Iowa Gambling Task performance across two independent studies: SPAD (August 2017 to May 2020); SUICIDE-DECIDE (March 2021 to September 2023). Data was analyzed between January 2025 and July 2026. Setting SPAD was conducted in Nîmes (France). SUICIDE-DECIDE was a muticenter study across Jena (Germany) and Nîmes. Clinical groups were recruited from academic inpatient psychiatry units and outpatient clinics, and healthy controls through local advertisement. Participants SPAD included adults aged 60 years or older; SUICIDE-DECIDE middle-aged adults stratified by attempt means (violent/nonviolent). Consecutive participants were recruited into three groups: suicide attempters, patient controls, healthy controls. Main outcomes and measures The primary outcomes were group differences on parameters reflecting learning from rewards and losses, sensitivity to win frequency, choice perseverance, and forgetting of past choices. We trained a multinomial logistic regression model in the SUICIDE-DECIDE study using self-reported depression and parameters that differentiated attempters from controls across studies, and evaluated its performance in the SPAD sample. Results SPAD included 57 participants (40 females [70.2%]; mean [SD] age, 70.1 [8.2]). SUICIDE-DECIDE included 124 participants (77 females [62.1%]; 32.1 [11.0]). Suicide attempters forgot past choices faster than controls (SPAD: F (2,36) = 13.5; P F (3,57) = 46.6; P Conclusions and relevance. Short-sighted decision-making may represent a cognitive phenotype associated with suicide in high-risk populations and a potential target for prevention and treatment. Key points Question Which computational decision-making mechanisms are specifically disrupted in high-risk suicide attempters, and can they improve discrimination from depressed and healthy controls? Findings Across two independent case-control studies, suicide attempters consistently showed significantly greater forgetting of past choices. This computational parameter improved participant classification compared to depression severity alone, achieving a balanced accuracy in an independent validation sample of 100% for healthy controls, 85.95% for depressed controls, and 85.44% for suicide attempters. Meaning Greater forgetting of past choices may represent a reproducible cognitive phenotype associated with suicide risk that could improve risk stratification and targeted preventive interventions.