Emotions related to threatening events are mainly linked to the right hemisphere
Bibliographic record
Abstract
A recent meta-analysis of functional neuroimaging contrasts between emotional and neutral face processing has shown that the processing of facial emotions can be better classified according to threat detection than emotional valence, with the authors suggesting that their data are inconsistent with both the right-hemisphere and valence models of emotional laterality. I report empirical and theoretical data indicating that facial expressions are better classified according to threat detection than to the distinction between positive and negative emotions. I challenge, however, the claim that laterality effects provide little support to the right-hemisphere model of emotional laterality. This claim contrasts with neuropsychological and psychophysiological investigations that have shown that the right hemisphere has a graded prevalence for emotions provoked by threatening events. A reanalysis of data obtained in the target study suggests that the reported data are not necessarily inconsistent with a model assuming a graded, right-hemisphere dominance for emotions. I present a model of hemispheric asymmetries that could be consistent with the assumption that the right hemisphere's dominance for emotions may mainly be concerned with threatening events.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".