Exploring the impact of awe on the multifaceted construct of empathy.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it