Sensorimotor learning during synchronous speech is modulated by the acoustics of the other voice
Bibliographic record
Abstract
This study tested the hypothesis that speaking with other voices can influence sensorimotor predictions of one's own voice. Real-time manipulations of auditory feedback were used to drive sensorimotor adaptation in speech, while participants spoke sentences in synchrony with another voice, a task known to induce implicit imitation (phonetic convergence). The acoustic-phonetic properties of the other voice were manipulated between groups, such that convergence with it would either oppose (incongruent group, n = 15) or align with (congruent group, n = 16) speech motor adaptation. As predicted, significantly greater adaptation was seen in the congruent compared to the incongruent group. This suggests the use of shared sensory targets in speech for predicting the sensory outcomes of both the actions of others (speech perception) and the actions of the self (speech production). This finding has important implications for wider theories of shared predictive mechanisms across perception and action, such as active inference.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.014 | 0.003 |
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; both teacher heads agree on what is shown here.
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".