The role of emotional awareness in evaluative judgment: evidence from alexithymia
Bibliographic record
Abstract
Evaluative judgments imply positive or negative regard. But there are different ways in which something can be positive or negative. How do we tell them apart? According to Evaluative Sentimentalism, different evaluations (e.g., dangerousness vs. offensiveness) are grounded on different emotions (e.g., fear vs. anger). If this is the case, evaluation differentiation requires emotional awareness. Here, we test this hypothesis by looking at alexithymia, a deficit in emotional awareness consisting of problems identifying, describing, and thinking about emotions. The results of Study 1 suggest that high alexithymia is not only related to problems distinguishing emotions, but also to problems distinguishing evaluations. Study 2 replicated this latter effect after controlling for individual differences in attentional impulsiveness and reflective reasoning, and found that reasoning makes an independent contribution to evaluation differentiation. These results suggest that emotional sensibilities play an irreducible role in evaluative judgment while affording a role for reasoning.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".