Neural Responses to Consciously and Unconsciously Perceived Emotional Faces: A Spinal fMRI Study
Bibliographic record
Abstract
Emotional stimuli modulate activity in brain areas related to attention, perception, and movement. Similar increases in neural activity have been detected in the spinal cord, suggesting that this understudied component of the central nervous system is an important part of our emotional responses. To date, previous studies of emotion-dependent spinal cord activity have utilized long presentations of complex emotional scenes. The current study differs from this research by (1) examining whether emotional faces will lead to enhanced spinal cord activity and (2) testing whether these stimuli require conscious perception to influence neural responses. Fifteen healthy undergraduate participants completed six spinal functional magnetic resonance imaging (fMRI) runs in which three one-minute blocks of fearful, angry, or neutral faces were interleaved with 40-s rest periods. In half of the runs, the faces were clearly visible while in the other half, the faces were displayed for only 17 ms. Spinal fMRI consisted of half-Fourier acquisition single-shot turbo spin-echo (HASTE) sequences targeting the cervical spinal cord. The results indicated that consciously perceived faces expressing anger elicited significantly more activity than fearful or neutral faces in ventral (motoric) regions of the cervical spinal cord. When stimuli were presented below the threshold of conscious awareness, neutral faces elicited significantly more activity than angry or fearful faces. Together, these data suggest that the emotional modulation of spinal cord activity is most impactful when the stimuli are consciously perceived and imply a potential threat toward the observer.
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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 | 0.000 |
| 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) | 0.002 | 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".