Contextual cuing contributes to the independent modification of multiple internal models for vocal control
Bibliographic record
Abstract
Research on the control of visually guided limb movements indicates that the brain learns and continuously updates an internal model that maps the relationship between motor commands and sensory feedback. A growing body of work suggests that an internal model that relates motor commands to sensory feedback also supports vocal control. There is evidence from arm-reaching studies that shows that when provided with a contextual cue, the motor system can acquire multiple internal models, which allows an animal to adapt to different perturbations in diverse contexts. In this study we show that trained singers can rapidly acquire multiple internal models regarding voice fundamental frequency (F(0)). These models accommodate different perturbations to ongoing auditory feedback. Participants heard three musical notes and reproduced each one in succession. The musical targets could serve as a contextual cue to indicate which direction (up or down) feedback would be altered on each trial; however, participants were not explicitly instructed to use this strategy. When participants were gradually exposed to altered feedback adaptation was observed immediately following vocal onset. Aftereffects were target specific and did not influence vocal productions on subsequent trials. When target notes were no longer a contextual cue, adaptation occurred during altered feedback trials and evidence for trial-by-trial adaptation was found. These findings indicate that the brain is exceptionally sensitive to the deviations between auditory feedback and the predicted consequence of a motor command during vocalization. Moreover, these results indicate that, with contextual cues, the vocal control system may maintain multiple internal models that are capable of independent modification during different tasks or environments.
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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 | 0.000 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".