Negative valuation of ambiguous feedback may predict near-term risk for suicide attempt in Veterans at high risk for suicide
BackgroundLearning 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...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Psychiatry |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1492332/full |
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| author | Catherine E. Myers Catherine E. Myers Rokas Perskaudas Rokas Perskaudas Vibha Reddy Chintan V. Dave Chintan V. Dave John G. Keilp Arlene King Kailyn Rodriguez Kailyn Rodriguez Lauren St. Hill Rachael Miller Alejandro Interian Alejandro Interian |
| author_facet | Catherine E. Myers Catherine E. Myers Rokas Perskaudas Rokas Perskaudas Vibha Reddy Chintan V. Dave Chintan V. Dave John G. Keilp Arlene King Kailyn Rodriguez Kailyn Rodriguez Lauren St. Hill Rachael Miller Alejandro Interian Alejandro Interian |
| author_sort | Catherine E. Myers |
| collection | DOAJ |
| description | BackgroundLearning 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.MethodVeterans (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.ResultsOptimal 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.ConclusionA 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. |
| format | Article |
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| institution | Kabale University |
| issn | 1664-0640 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
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| spelling | doaj-art-7bc8b046d06e4433b98a5858591a0a1f2025-01-30T06:22:05ZengFrontiers Media S.A.Frontiers in Psychiatry1664-06402025-01-011510.3389/fpsyt.2024.14923321492332Negative valuation of ambiguous feedback may predict near-term risk for suicide attempt in Veterans at high risk for suicideCatherine E. Myers0Catherine E. Myers1Rokas Perskaudas2Rokas Perskaudas3Vibha Reddy4Chintan V. Dave5Chintan V. Dave6John G. Keilp7Arlene King8Kailyn Rodriguez9Kailyn Rodriguez10Lauren St. Hill11Rachael Miller12Alejandro Interian13Alejandro Interian14Research Service, VA New Jersey Health Care System, Department of Veterans Affairs, East Orange, NJ, United StatesDepartment of Pharmacology, Physiology and Neuroscience, New Jersey Medical School, Rutgers University, Newark, NJ, United StatesMental Health and Behavioral Services, VA New Jersey Health Care System, Department of Veterans Affairs, Lyons, NJ, United StatesWar Related Illness and Injury Study Center (WRIISC), East Orange, NJ, United StatesResearch Service, VA New Jersey Health Care System, Department of Veterans Affairs, East Orange, NJ, United StatesResearch Service, VA New Jersey Health Care System, Department of Veterans Affairs, East Orange, NJ, United StatesCenter for Pharmacoepidemiology and Treatment Science, Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ, United StatesDepartment of Psychiatry, Columbia University College of Physicians and Surgeons, New York State Psychiatric Institute, New York, NY, United StatesMental Health and Behavioral Services, VA New Jersey Health Care System, Department of Veterans Affairs, Lyons, NJ, United StatesResearch Service, VA New Jersey Health Care System, Department of Veterans Affairs, East Orange, NJ, United StatesDepartment of Psychology, Rutgers University School of Arts and Sciences, Piscataway, NJ, United StatesMental Health and Behavioral Services, VA New Jersey Health Care System, Department of Veterans Affairs, Lyons, NJ, United StatesMental Health and Behavioral Services, VA New Jersey Health Care System, Department of Veterans Affairs, Lyons, NJ, United StatesMental Health and Behavioral Services, VA New Jersey Health Care System, Department of Veterans Affairs, Lyons, NJ, United StatesDepartment of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ, United StatesBackgroundLearning 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.MethodVeterans (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.ResultsOptimal 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.ConclusionA 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.https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1492332/fullsuicidefeedback learningreinforcement learningcomputational modelsoftware |
| spellingShingle | Catherine E. Myers Catherine E. Myers Rokas Perskaudas Rokas Perskaudas Vibha Reddy Chintan V. Dave Chintan V. Dave John G. Keilp Arlene King Kailyn Rodriguez Kailyn Rodriguez Lauren St. Hill Rachael Miller Alejandro Interian Alejandro Interian Negative valuation of ambiguous feedback may predict near-term risk for suicide attempt in Veterans at high risk for suicide Frontiers in Psychiatry suicide feedback learning reinforcement learning computational model software |
| title | Negative valuation of ambiguous feedback may predict near-term risk for suicide attempt in Veterans at high risk for suicide |
| title_full | Negative valuation of ambiguous feedback may predict near-term risk for suicide attempt in Veterans at high risk for suicide |
| title_fullStr | Negative valuation of ambiguous feedback may predict near-term risk for suicide attempt in Veterans at high risk for suicide |
| title_full_unstemmed | Negative valuation of ambiguous feedback may predict near-term risk for suicide attempt in Veterans at high risk for suicide |
| title_short | Negative valuation of ambiguous feedback may predict near-term risk for suicide attempt in Veterans at high risk for suicide |
| title_sort | negative valuation of ambiguous feedback may predict near term risk for suicide attempt in veterans at high risk for suicide |
| topic | suicide feedback learning reinforcement learning computational model software |
| url | https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1492332/full |
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