Catherine E Myers ^1,2* Rokas Perskaudas ¹ Vibha Reddy ¹ Chintan V Dave ³ John G Keilp ⁴ Arlene King ¹ Kailyn Rodriguez ^1,5 Lauren St Hill ¹ Rachael Miller ¹ Alejandro Interian ^1,6*

• ¹ Research, VA New Jersey Health Care System, Veterans Health Administration, United States Department of Veterans Affairs, East Orange, NJ, United States
• ² Department of Pharmacology, Physiology & Neuroscience, New Jersey Medical School, Rutgers, The State University of New Jersey, Newark, New Jersey, United States
• ³ Institute for Health, Health Care Policy and Aging Research, Rutgers, The State University of New Jersey, New Brunswick, New Jersey, United States
• ⁴ Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, New York, United States
• ⁵ Department of Psychology, School of Arts and Sciences, Rutgers, The State University of New Jersey, Rutgers, New Jersey, United States
• ⁶ Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, New Jersey, United States

Background: Learning from feedback -adapting behavior based on reinforcing and punishing outcomes -has been implicated in numerous psychiatric disorders, including substance misuse, post-traumatic stress disorder, and depression; an emerging literature suggests it may also play a role in suicidality. This study examined whether a feedback-based learning task with rewarding, punishing and ambiguous outcomes, followed by computational modeling, could improve near-term prospective prediction of suicide attempt in a high-risk sample. Method: Veterans (N=60) at high-risk for suicide were tested on a task of reward-and punishment-based learning, at multiple sessions across a one-year period. Each session was coded according to whether the participant had (1) an actual suicide attempt (ASA); (2) another suicide-related event (OtherSE) such as suicidal behavior or suicidal ideation-related hospital admission (but not an ASA); or (3) neither (noSE) in the next 90 days. Computational modeling was used to estimate latent cognitive variables including learning rates from positive and negative outcomes, and the subjective value of ambiguous feedback. Results: Optimal responding on the reward-based trials was positively associated with upcoming ASA, and remained predictive even after controlling for other standard clinical variables such as current suicidal ideation severity and prior suicide attempts. Computational modeling revealed that patients with upcoming ASA tended to view ambiguous outcomes as similar to weak punishment, while OtherSE and noSE both tended to view the ambiguous outcome as similar to weak reward. Differences in the reinforcement value of the neutral outcome remained predictive for ASA even after controlling for current suicidal ideation and prior suicide attempts. Conclusion: A reinforcement learning task with ambiguous neutral outcomes may provide a useful tool to help predict near-term risk of ASA in at-risk patients. While most individuals interpret ambiguous feedback as mildly reinforcing (a "glass half full" interpretation), those with upcoming ASA tend to view it as mildly punishing (a "glass half empty" interpretation). While the current results are based on a very small sample with relatively few ASA events, and require replication in a larger sample, they provide support for the role of negative biases in feedback-based learning in the cognitive profile of suicide risk.