Examining the Role of Physiological Arousal in Laboratory Risk-Taking in Social and Non-Social Contexts
Bibliographic record
Abstract
Current theoretical models attribute the rise in risk-taking during adolescence to heightened activity in reward processing brain regions when in the presence of social and non-social rewarding stimuli. However, non-rewarding, but very salient stimuli, have also been shown to increase activity in reward processing brain regions and could, in theory, also increase risk taking propensity in adolescents. To examine this, we had participants complete a risk-taking task under “standard” conditions as well as under one of three experimental conditions: virtual peer observer with positive social feedback (positive social), virtual peer observer with neutral social feedback (negative social), and with triple the potential rewards (non-social positive). The study’s sample consisted of 59 mainly young adult participants (Mage = 20.69, SD = 5.08), where 22 identified as men and 37 identified as women. A multi-level model revealed no overall effect of exposure to the experimental context on risk-taking. Greater skin conductance was, unexpectedly, associated with less risk-taking. When examining each context in separate models, exposure to the non-social showed associations with increased risk-taking, whereas the positive social context did not. Negative social contexts showed a pattern of means suggesting that exposure to such contexts may be associated with increased risk-taking, but our models may have been underpowered and were unable to detect this effect. These findings suggest that the salience of a context may be an important factor to consider when exploring what drives adolescent risk-taking.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".