Author response: Somatic and vicarious pain are represented by dissociable multivariate brain patterns
Bibliographic record
Abstract
The ability to experience others’ pain is a cornerstone of empathy, and binds us together in times of hardship. However, we have not yet fully understood the complex interactions in the brain that make people empathetic to others’ suffering. One possibility is that we experience others’ pain through the activation of the same brain regions as those that enable us to experience physical pain ourselves. To test this idea, Krishnan et al. compared patterns of brain activity in human volunteers as they experienced pain (from heat being applied to their forearm or foot) or watched images of others’ hands or feet being injured. While watching these images, the volunteers were asked to try to imagine that the injuries were happening to their own bodies. The patterns of brain activity that arose when the volunteers observed someone else in pain did not overlap with the patterns produced when the volunteers experienced pain themselves. Instead, seeing someone else in pain activated regions involved in taking another person’s perspective. This process, which is known as mentalizing, involves thinking about the other person’s thoughts, intentions and preferences. Thus within the brain, the experience of observing someone else in pain is distinct from that of experiencing physical pain in oneself. The results presented by Krishnan et al. raise new questions about how the brain regions involved in empathy help us to relate to other people when they experience different types of pain. Future studies should explore the factors that influence our ability to adopt another’s perspective, and whether it might be possible to improve this ability.
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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 | 0.003 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.084 | 0.029 |
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".