The Emotionally Intelligent Decision Maker
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In two experiments, we examined how a core dimension of emotional intelligence, emotion-understanding ability, facilitates decision making. Individuals with higher levels of emotion-understanding ability can correctly identify which events caused their emotions and, in particular, whether their emotions stem from events that are unrelated to current decisions. We predicted that incidental feelings of anxiety, which are unrelated to current decisions, would reduce risk taking more strongly among individuals with lower rather than higher levels of emotion-understanding ability. The results of Experiment 1 confirmed this prediction. In Experiment 2, the effect of incidental anxiety on risk taking among participants with lower emotion-understanding ability, relative to participants with higher emotion-understanding ability, was eliminated when we informed participants about the source of their anxiety. This finding reveals that emotion-understanding ability guards against the biasing effects of incidental anxiety by helping individuals determine that such anxiety is irrelevant to current decisions.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.010 |
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 it