Auditory Motor Integration in Oral and Manual Effectors
Bibliographic record
Abstract
Torrey M. J. Loucksa, Edward Oforib, Christopher M. Grindroda, Luc F. De Nilc & Jacob J. Sosnoffb a Department of Speech and Hearing Science , University of Illinois , Urbana-Champaign b Department of Kinesiology and Community Health , University of Illinois , Urbana-Champaign c Department of Speech-Language Pathology , University of Toronto , Ontario, Canada ABSTRACT Sensorimotor integration of auditory feedback for oral and manual force control was compared in 10 healthy participants. Based on the notion that auditory-to-motor integration is a more typical form of feedback for oral articulators given their role in speech and singing, it was predicted that oral force generation would be more accurate and less variable on an auditory-motor task compared to manual force generation. However, finger force production showed similar accuracy and lower variability than lip force production. The authors propose that auditory feedback can be used for fine force control of both oral and manual effectors. Differences in performance are considered to arise from physiological differences between the effectors that are reflected in their typical functions. This novel study of oral and manual force control under auditory feedback is an important step in understanding how auditory information can be associated with fine force control.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".