Self-esteem and risky decision-making: An ERP study
Bibliographic record
Abstract
Self-esteem, a value one places on oneself, influences one's cognitive, emotional and behavioral responses across various situations. In the case of risky decision-making, high self-esteem (SE) individuals rely on their positive self-views and tend to be less defensive in response to a risky task; low SE individuals, on the contrary, tend to have fewer accessible positive resources and thus, are more prone to risk-aversion. While past studies have provided evidence for a link between self-esteem and a behaviorally-risky response, no study has explored the relation between self-esteem and the electrophysiological correlates of risky response. Therefore, the current study investigated the correlates of risky decision-making in high SE compared to low SE participants using event-related potentials (ERP) technology in 28 undergraduate students playing a blackjack game. The results showed that there was no difference between the high SE participants and the low SE participants with respect to the behavioral assessments of the risk-taking decision-making. However, for the electrophysiological data, we observed that the amplitude of P2 (150-300 ms) was more positive in the high SE participants compared to the low SE participants over the central-posterior scalp region. Dipole source analysis indicated that this positive component was generated in posterior cingulate cortex (PCC). These findings suggest that the high SE participants experienced more emotional signals than the low SE participants during decision-making.
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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 | 0.000 | 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".