The functional relations among motor-based prediction, sensory goals and feedback in learning non-native speech sounds: Evidence from adult Mandarin Chinese speakers with an auditory feedback masking paradigm
Bibliographic record
Abstract
Previous studies in speech production and acquisition have mainly focused on how feedback vs. goals and feedback vs. prediction regulate learning and speech control. The present study investigated the less studied mechanism-prediction vs. goals in the context of adult Mandarin speakers' acquisition of non-native sounds, using an auditory feedback masking paradigm. Participants were asked to learn two types of non-native vowels: /ø/ and /ɵ/-the former being less similar than the latter to Mandarin vowels, either in feedback available or feedback masked conditions. The results show that there was no significant improvement in learning the two targets when auditory feedback was masked. This suggests that motor-based prediction could not directly compare with sensory goals for adult second language acquisition. Furthermore, auditory feedback can help achieve learning only if the competition between prediction and goals is minimal, i.e., when target sounds are distinct from existing sounds in one's native speech. The results suggest motor-based prediction and sensory goals may share a similar neural representational format, which could result in a competing relation in neural recourses in speech learning. The feedback can conditionally overcome such interference between prediction and goals. Hence, the present study further probed the functional relations among key components (prediction, goals and feedback) of sensorimotor integration in speech learning.
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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 | 0.002 |
| 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 | 0.001 |
| 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".