Neural response to rewards predicts risk-taking in late but not early adolescent females
Bibliographic record
Abstract
Risk-taking peaks in adolescence and reflects, in part, hyperactivity of the brain's reward system. However, it has not been established whether the association between reward-related brain activity and risk-taking varies across adolescence. The present study investigated how neural reward sensitivity is associated with laboratory risk-taking in a sample of female adolescents as a function of age. Sixty-three female adolescents ages 10-19 completed the Balloon Analogue Risk Task, a laboratory measure of risk-taking behavior, as well as a forced choice monetary gambling task while an electroencephalogram (EEG) was recorded. This gambling task elicits the reward positivity (RewP), a frontocentral event-related potential component that is sensitive to feedback signaling reward. We observed a negative quadratic association between age and risk-taking, such that those in early and late adolescence had lower relative risk-taking compared to mid-adolescence, with risk-taking peaking at around 15 years of age. In predicting risk-taking, we observed an interaction between age and RewP, such that reward-related brain activity was not associated with risk-taking in early adolescence but was associated with a greater propensity for risk in later adolescence. These findings suggest that for females, neural response to rewards is an important factor in predicting risk-taking only in later adolescence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".