Somatosensory Contribution to Motor Learning Due to Facial Skin Deformation
Bibliographic record
Abstract
Motor learning is dependent on kinesthetic information that is obtained both from cutaneous afferents and from muscle receptors. In human arm movement, information from these two kinds of afferents is largely correlated. The facial skin offers a unique situation in which there are plentiful cutaneous afferents and essentially no muscle receptors and, accordingly, experimental manipulations involving the facial skin may be used to assess the possible role of cutaneous afferents in motor learning. We focus here on the information for motor learning provided by the deformation of the facial skin and the motion of the lips in the context of speech. We used a robotic device to slightly stretch the facial skin lateral to the side of the mouth in the period immediately preceding movement. We found that facial skin stretch increased lip protrusion in a progressive manner over the course of a series of training trials. The learning was manifest in a changed pattern of lip movement, when measured after learning in the absence of load. The newly acquired motor plan generalized partially to another speech task that involved a lip movement of different amplitude. Control tests indicated that the primary source of the observed adaptation was sensory input from cutaneous afferents. The progressive increase in lip protrusion over the course of training fits with the basic idea that change in sensory input is attributed to motor performance error. Sensory input, which in the present study precedes the target movement, is credited to the target-related motion, even though the skin stretch is released prior to movement initiation. This supports the idea that the nervous system generates motor commands on the assumption that sensory input and kinematic error are in register.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".