The Role of Instructions in Motor Learning of Oral Versus Nasalized Speech Targets
Bibliographic record
Abstract
Purpose: The purpose of this study was to investigate how general, implicit instructions with auditory-perceptual emphasis; specific, explicit instructions with biomechanical focus; or both affect learning of oral–nasal balance control in speech. Method: Thirty healthy, vocally untrained participants were assigned to one of three instructional groups (i.e., implicit, explicit, and integrated) and learned to produce oral versus nasalized vowel-, syllable-, and phrase-level targets during once-weekly sessions over 4 weeks. Learning gains and performance variability were analyzed using nasometry. Results: We observed a significant main effect of instruction type on learning gains at phrase level ( p = .016). Specifically, the integrated group ( M = 59.8%) significantly outperformed the explicit group ( M = 37.9%) and numerically outperformed the implicit group ( M = 45.1%). For nasalized phrase targets, results revealed a significant main effect of instruction type on performance variability ( p = .042), but pairwise comparisons between instruction groups were not significant. Conclusions: The integration of implicit processes via auditory-perceptual modeling and explicit processes via relevant biomechanical directives resulted in larger motor learning gains, especially at higher levels of task complexity (i.e., phrase) compared to providing implicit or explicit instruction alone. The higher performance variability (i.e., less stable productions) that was sometimes induced by explicit instruction did not negatively impact learning when integrated with implicit instruction. Clinical implications for speech/voice therapy models are discussed.
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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 | 0.005 |
| 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) | 0.001 | 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".