Towards understanding the low correlation between subjective and performance-based measures of emotion perception: Is one measure better than the other?
Bibliographic record
Abstract
This study examined the construct validity of performance-based emotion recognition tests. We recruited 227 adults (30-60 years old) through Prolific to complete four emotion recognition tasks in addition to measures of self-reported emotion recognition, crystallized intelligence, social confidence, loneliness/well-being, interest in people versus things, and reading enjoyment (all measured with at least two indicators). Consistent with previous research, objective emotion recognition tasks were positively correlated and formed a separate cluster. This cluster correlated with crystallized intelligence but not with self-reported emotion recognition, loneliness/well-being, interest in people vs. things, or reading pleasure. Interestingly, there appeared to be a negative correlation with social self-confidence, suggesting that people who performed well on emotion recognition tasks had less social self-confidence. This is consistent with the possibility that hypersensitivity to social cues may have disadvantages. Conversely, self-reported skill in recognizing emotions correlated highly with social self-confidence and loneliness/well-being, at least in a context where no clear advantage existed for specific response patterns. Overall, our findings suggest that the objective tests primarily assessed intelligence rather than broader social-emotional functioning. In brief, this study highlights the importance of selecting appropriate measures when evaluating individuals’ emotional intelligence.
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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 | 0.024 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".