Adolescents let sufficient evidence accumulate before making a decision when large incentives are at stake
Bibliographic record
Abstract
Adolescent decision-making has been described as impulsive and suboptimal in the presence of incentives. In this study we examined the neural substrates of adolescent decision-making using a perceptual discrimination task for which small and large rewards were associated with correctly detecting the direction of motion of a cloud of moving dots. Adults showed a reward bias of faster reaction times on trials for which the direction of motion was associated with a large reward. Adolescents, in contrast, were slower to make decisions on trials associated with large rewards. This behavioral pattern in adolescents was paralleled by greater recruitment of fronto-parietal regions important in representing the accumulation of evidence sufficient for selecting one choice over its alternative and the certainty of that choice. The findings suggest that when large incentives are dependent on performance, adolescents may require more evidence to accumulate prior to responding, to be certain to maximize their gains. Adults, in contrast, appear to be quicker in evaluating the evidence for a decision when primed by rewards. Overall these findings suggest that rather than reacting hastily, adolescents can be incentivized to take more time to make decisions when large rewards are at stake. A video abstract of this article can be viewed at http://youtu.be/1g4F5vzFDl0.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".