Mood computational mechanisms underlying increased risk behavior in adolescent suicidal patients
Bibliographic record
Abstract
Abstract Suicidal thoughts and behaviors (STB) rank among the foremost causes of death globally. While literature consistently highlights increased risk behavior in individuals with STB and identifies mood issues as central to STB, the precise cognitive and affective computational mechanisms driving this increased risky behavior remain elusive. Here, we asked 83 adolescent inpatients with affective disorders, where 58 patients with STB (S+) and 25 without STB (S−), and 118 gender/age-matched healthy control (HC) to make decisions between certain vs. gamble option with momentary mood ratings. Choice data analyses revealed more risk behavior in S+ compared to S− and HC. Using a prospect theory model enhanced with approach-avoidance parameters revealed that this rise in risky behavior resulted only from a heightened approach parameter in S+. Furthermore, approach strength mediated the rise in gambling choices with STB severity. Altogether, model-based choice data analysis indicated dysfunction in the approach system in S+ individuals, leading to greater propensity for gambling in favorable outcomes regardless the lotteries expected value. Additionally, mood model-based analyses revealed reduced sensitivity to certain rewards in S+ compared to S− and HC. Importantly, these computational markers generalized to healthy population (n = 747). In S+, mood sensitivity to certain reward was negatively correlated with gambling, offering a mood computational account for increased risk behavior in STB. These findings remained significant even after adjusting for demographic, clinical, and medication-related variables. Overall, our study uncovers the cognitive and affective mechanisms contributing to increased risk behavior in STB, with significant implications for suicide prevention.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".