Sound elicits stereotyped facial movements that provide a sensitive index of hearing abilities
Bibliographic record
Abstract
SUMMARY Sound elicits rapid movements of muscles in the face, ears, and eyes that protect the body from injury and trigger brain-wide internal state changes. Here, we performed quantitative facial videography from mice resting atop a piezoelectric force plate and observed that broadband sounds elicit rapid, small, and highly stereotyped movements of a facial region near the vibrissae array. Facial motion energy (FME) analysis revealed sensitivity to far lower sound levels than the acoustic startle reflex and greater reliability across trials and mice than sound-evoked pupil dilations or movement of other facial and body regions. FME tracked the low-frequency envelope of sounds and could even decode speech phonemes in varying levels of background noise with high accuracy. FME growth slopes were disproportionately steep in mice with autism risk gene mutations and noise-induced sensorineural hearing loss, providing an objective behavioral measure of sensory hyper-responsivity. Increased FME after noise-induced cochlear injury was closely associated with the emergence of excess gain in later waves of the auditory brainstem response, suggesting a midbrain contribution. Deep layer auditory cortex units were entrained to spontaneous facial movements but optogenetic suppression of cortical activity facilitated – not suppressed – sound-evoked FME, suggesting the auditory cortex is a modulator rather than a mediator of sound-evoked facial movements. These findings highlight a simple involuntary behavior that is more sensitive and integrative than other auditory reflex pathways and captures higher-order changes in sound processing from mice with inherited and acquired hearing disorders.
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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 | 0.001 | 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 | 0.001 |
| 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".