Evidence map›Paper›PMID 39949497›Full record

ArticleFrontiers in psychiatry2024

Negative valuation of ambiguous feedback may predict near-term risk for suicide attempt in Veterans at high risk for suicide.

Catherine E Myers, Rokas Perskaudas, Vibha Reddy, Chintan V Dave, John G Keilp, Arlene King, Kailyn Rodriguez, Lauren St Hill, Rachael Miller, Alejandro Interian

Abstract read
In one paragraph

Article in Frontiers in psychiatry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Catherine E Myers *Research Service, VA New Jersey Health Care System, Department of Veterans Affairs, East Orange, NJ, United States.
Rokas PerskaudasMental Health and Behavioral Services, VA New Jersey Health Care System, Department of Veterans Affairs, Lyons, NJ, United States.
Vibha ReddyResearch Service, VA New Jersey Health Care System, Department of Veterans Affairs, East Orange, NJ, United States.
Chintan V DaveResearch Service, VA New Jersey Health Care System, Department of Veterans Affairs, East Orange, NJ, United States.
John G KeilpDepartment of Psychiatry, Columbia University College of Physicians and Surgeons, New York State Psychiatric Institute, New York, NY, United States.
Arlene KingMental Health and Behavioral Services, VA New Jersey Health Care System, Department of Veterans Affairs, Lyons, NJ, United States.
Kailyn RodriguezResearch Service, VA New Jersey Health Care System, Department of Veterans Affairs, East Orange, NJ, United States.
Lauren St HillMental Health and Behavioral Services, VA New Jersey Health Care System, Department of Veterans Affairs, Lyons, NJ, United States.
Rachael MillerMental Health and Behavioral Services, VA New Jersey Health Care System, Department of Veterans Affairs, Lyons, NJ, United States.
Alejandro Interian *Mental Health and Behavioral Services, VA New Jersey Health Care System, Department of Veterans Affairs, Lyons, NJ, United States.

Funding

CSRD VA I01 CX001826
6 · The paper itself

Abstract

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.

Indexed as

computational modelfeedback learningreinforcement learningsoftwaresuicide

Identifiers

PMID39949497
PMCPMC11821650

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.