Generating facial expressions of disgust activates neurons in the thoracic spinal cord: a spinal fMRI study
Bibliographic record
Abstract
Facial expressions of disgust, which involve movement of the levator labii muscles on the nose, allow an organism to restrict the intake of potentially aversive stimuli by constricting the air cavities in the nostrils and reducing the speed of air intake. In the current research, we used fMRI of the thoracic spinal cord to measure neural activity related to (1) the contraction of the intercostal muscles that modulate the velocity of air intake and (2) the sensory feedback associated with this contraction. Thirteen participants completed two spinal fMRI runs in which the thoracic segments of the spinal cord were measured. Each five-minute 40-second run consisted of three 60-second blocks in which participants repeatedly generated a disgusted facial expression or a non-emotional expression consisting of repeated stretching of the lips (which did not involve the nasal cavity). Forty-second rest blocks were interleaved between each expression block. The results demonstrated that generating emotional expressions of disgust produces significantly more activity than producing non-emotional facial expressions. This activity occurred in both ventral (motoric) and dorsal (sensory) regions of the upper segments of the thoracic spinal cord and demonstrates a link between the generation of facial expressions and embodied emotional responses.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".