When to Use Your Head and When to Use Your Heart
Bibliographic record
Abstract
Four studies explored whether perspective-taking and empathy would be differentially effective in mixed-motive competitions depending on whether the critical skills for success were more cognitively or emotionally based. Study 1 demonstrated that individual differences in perspective-taking, but not empathy, predicted increased distributive and integrative performance in a multiple-round war game that required a clear understanding of an opponent's strategic intentions. Conversely, both measures and manipulations of empathy proved more advantageous than perspective-taking in a relationship-based coalition game that required identifying the strength of interpersonal connections (Studies 2-3). Study 4 established a key process: perspective-takers were more accurate in cognitive understanding of others, whereas empathy produced stronger accuracy in emotional understanding. Perspective-taking and empathy were each useful but in different types of competitive, mixed-motive situations-their success depended on the task-competency match. These results demonstrate when to use your head versus your heart to achieve the best outcomes for oneself.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".