Empathy for joy recruits a broader prefrontal network than empathy for sadness and is predicted by executive functioning.
Bibliographic record
Abstract
Empathy encompasses the ability to contemplate and vicariously share in the emotional life of others, and is critical for social interaction, and may enhance subjective happiness. OBJECTIVE: While a few theoretical models propose that executive function may play a role in empathy, it is unknown how variation in executive function, and underlying variation in key large-scale brain network nodes, such as the dorsolateral prefrontal cortex node within the executive control network-or the medial prefrontal cortex (PFC) node within the mentalizing/theory of mind network-may account for individual differences in empathy capacity. METHOD: The relationship between individual differences in executive capacity-parsed into working memory, inhibition, and cognitive flexibility subdomains-and magnitude of activity in a priori identified PFC subregions during a functional MRI-based ecologically valid empathy induction paradigm, was investigated. Empathic happiness (i.e., vicarious joy) and empathic concern (i.e., vicarious sadness) in response to the life circumstances of actual people were measured at separate time points as brain functional MRI was obtained. Participants also completed executive-heavy clinical neuropsychological tasks outside of the scanner. RESULTS: Frontopolar PFC was activated across both types of empathy. However, empathic happiness related to engagement of a much broader network of prefrontal cortex subregions relative to empathic concern: spawning frontopolar, dorsolateral, and medial aspects. PFC activation during both types of empathy was positively predicted by working memory capacity. CONCLUSION: Activation in core aspects of the working memory-executive control network, and core happiness-related aspects of the mentalizing brain network (i.e., medial PFC and precuneus) predicted greater empathy capacity. (PsycInfo Database Record (c) 2021 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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".