Blunted neural response to gains versus losses associated with both risk‐prone and risk‐averse behavior in a clinically diverse sample
Bibliographic record
Abstract
Reduced responsiveness to reward has been associated with both risk-prone and risk-averse behavior, common features of externalizing and internalizing psychopathology, respectively. Thus, evidence has suggested a potential quadratic relationship (i.e., inverted U) between reward sensitivity and risk-taking propensity. Blunted response to reward compared to loss may therefore demonstrate transdiagnostic utility as it relates to different patterns of maladaptive risk behavior. The current study sought to disentangle the relationship between risk and reward in a clinically diverse sample. In a sample of 210 adults (aged 18-30), the RewP (an ERP indexing differentiation between rewards and losses) was measured during a monetary guessing game, and risk-taking propensity was measured with a behavioral task (i.e., BART) that simulates real-world risk taking. Participants also completed clinical assessments to assess for lifetime psychopathology. Results indicated that there was no linear association between the RewP and risk-taking propensity; however, there was a significant quadratic relationship. Thus, a reduced sensitivity to reward receipt was associated with both risk-prone and risk-averse behavior. There was also a significant quadratic relationship between the RewP and money won during the BART, indicating that being too risk prone or risk averse is disadvantageous and leads to missed reward. Overall, these findings suggested that blunted neural differentiation between gains and losses may contribute to deficits in effectively weighing reward and loss and result in maladaptive risk-taking behavior. These findings support continued examination of reward dysfunction dimensionally in order to better characterize behavioral profiles implicated in clinical phenotypes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".