Meta-Analysis of ERP Investigations of Pain Empathy underlines methodological issues in ERP research
Bibliographic record
Abstract
Abstract Empathy has received considerable attention from the field of cognitive and social neuroscience. A significant portion of these studies used the event-related potential (ERP) technique to study the mechanisms of empathy for pain in others in different conditions and clinical populations. These show that specific ERP components measured during the observation of pain in others are modulated by several factors and altered in clinical populations. However, issues present in this literature such as analytical flexibility and lack of type 1 error control raise doubts regarding the validity and reliability of these conclusions. The current study compiled the results and methodological characteristics of 40 studies using ERP to study empathy of pain in others. The results of the meta-analysis suggest that the centro-parietal P3 and late positive potential component are sensitive to the observation of pain in others, while the early N1 and N2 components are not reliably associated with vicarious pain observation. The review of the methodological characteristics shows that the presence of selective reporting, analytical flexibility and lack of type 1 error control compromise the interpretation of these results. The implication of these results for the study of empathy and potential solutions to improve future investigations are discussed.
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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 | 0.102 | 0.207 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.018 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".