Are Empathic People Better Adjusted? A Test of Competing Models of Empathic Accuracy and Intrapersonal and Interpersonal Facets of Adjustment Using Self- and Peer Reports
Bibliographic record
Abstract
Are individuals adept at perceiving others' emotions optimally adjusted? We extend past research by conducting a high-powered preregistered study that comprehensively tests five theoretical models of empathic accuracy (i.e., emotion-recognition ability) and self-views and intra- and interpersonal facets of adjustment in a sample of 1,126 undergraduate students from Canada and 2,205 informants. We obtained both self-reports and peer-reports of adjustment and controlled for cognitive abilities as a potential confounding variable. Empathic accuracy (but not self-views of that ability) was positively related to relationship satisfaction as rated by both participants and informants. Self-views about empathic accuracy (but not actual empathic accuracy) were positively related to life satisfaction as rated by both participants and informants. All associations held when we controlled for cognitive abilities.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".