The Ability to Follow Your Gut: Emotion-Understanding Ability Leverages Feelings to Avoid Risk
Bibliographic record
Abstract
Emotional intelligence facilitates decision-making about risk. We propose that emotionally intelligent individuals make better decisions because they adaptively use their immediate feelings as a source of information about different decision options. We examine whether emotion-understanding ability (a primary dimension of emotional intelligence) helps individuals rely on their skin-conductance responses as signals about the potential danger associated with risky decision options. By correctly identifying the source of their skin-conductance responses, individuals with higher emotion-understanding ability use their feelings as relevant information to avoid choosing risky decision options. As predicted, we find that individuals with higher emotion-understanding ability exhibited a stronger association between skin-conductance responses and avoidance of risky options in the Iowa Gambling Task, relative to individuals with lower emotion-understanding ability. We also find that emotional intelligence enhances decision-making independently of cognitive intelligence. These results suggest that emotional intelligence enables individuals to use their feelings adaptively to guide decisions about risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".