Brain Networks Involved in Viewing Angry Hands or Faces
Bibliographic record
Abstract
Most neuropsychological research on the perception of emotion concerns the perception of faces. Yet in everyday life, hand actions are also modulated by our affective state, revealing it, in turn, to the observer. We used functional magnetic resonance imaging (fMRI) to identify brain regions engaged during the observation of hand actions performed either in a neutral or an angry way. We also asked whether these are the same regions as those involved in perceiving expressive faces. During the passive observation of emotionally neutral hand movements, the fMRI signal increased significantly in dorsal and ventral premotor cortices, with the exact location of the 'peaks' distinct from those induced by face observation. Various areas in the extrastriate visual cortex were also engaged, overlapping with the face-related activity. When the observed hand action was performed with emotion, additional regions were recruited including the right dorsal premotor, the right medial prefrontal cortex, the left anterior insula and a region in the rostral part of the supramarginal gyrus bilaterally. These regions, except for the supramarginal gyrus, were also activated during the perception of angry faces. These results complement the wealth of studies on the perception of affect from faces and provide further insights into the processes involved in the perception of others underlying, perhaps, social constructs such as empathy.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".