Exploring the impact of awe on the multifaceted construct of empathy.
Bibliographic record
Abstract
Empathy helps us navigate social interactions and promotes prosocial behaviors like caregiving and helping. Here, we explored whether awe, a key self-transcendent and epistemic emotion, could encourage greater empathy across seven diverse student and community samples collected between 2020 and 2022. Empathy is a multifaceted construct; thus, we assessed performance on a range of empathy measures including perspective taking accuracy (Study 2), empathic accuracy (Study 3; preregistered), emotion contagion and compassion (Study 4). We also directly tested whether awe motivated people to empathize with others (Study 5; preregistered). Although dispositional awe was positively correlated with trait measures of empathy (Study 1), experimental inductions of awe did not improve performance on empathy measures or motivate people to empathize, compared to a control (Studies 2-5). However, a moderation effect emerged in which awe had divergent effects on empathy depending on participants' self-reported dispositional levels of cognitive empathy. Although effects only reached significance in two studies (Studies 3; preregistered and 4), an internal meta-analysis revealed that awe improved empathy for those high in dispositional cognitive empathy, while marginally reducing it among those low in dispositional cognitive empathy, compared to a control. These results suggest that awe may have polarizing effects on empathy depending on one's dispositional level of cognitive empathy and reveal a potentially important role of cognitive processes in linking awe and empathy. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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 | 0.007 | 0.018 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".