The specificity of sensorimotor learning: Generalization in auditory feedback adaptation
Bibliographic record
Abstract
An enduring question about sensorimotor learning is how specific the acquired input-output relationship is. In this presentation, we review a series of studies in which the stimuli and conditions during speech motor learning were manipulated to study when and if generalization occurs. In our studies, the first and second formants of vowels were shifted using a real-time signal processing system; when subjects spoke one vowel, they heard themselves saying another vowel. In response to this auditory feedback perturbation, talkers spontaneously compensated by producing formants in the opposite direction in frequency to the perturbation. These compensations persisted after feedback was returned to normal, indicating that a form of sensorimotor learning had taken place. When different vowels were tested following the perturbation of one vowel, they were found not to show evidence of the feedback perturbation nor did the new vowels have any influence on the perturbed vowels’ return to normal baseline levels. Data from studies in which listening versus producing were compared and studies in which the similarity of the feedback voice quality was manipulated will also be presented. In general, the studies suggest that learning is quite local and, thus, that learning does not generalize beyond restricted conditions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".