Language enables the acquisition of distinct sensorimotor memories for speech
Bibliographic record
Abstract
Interactions between the context in which a sensorimotor skill is learned and the recall of that memory have been primarily studied in limb movements, but speech production requires movement, and many aspects of speech processing are influenced by task-relevant contextual information. Here, in ecologically valid speech (read sentences), we test whether English-French bilinguals can use the language of production to acquire and recall distinct motor plans for similar speech sounds spanning the production workspace. Participants experienced real-time alterations of auditory feedback while producing interleaved English and French sentences. The alterations were equal in magnitude but opposite in direction between languages. Over three experiments (n = 15 in each), we observed language-specific sensorimotor learning in speech that countered the alterations and persisted after the alterations were removed. The effects were not observed in a fourth experiment (n = 15) when the feedback alterations were tied to a non-linguistic cue. In a fifth experiment (n = 15), we provide further confirmation that the observed language-specific changes in speech production were confined to sentence production, the linguistic level at which they were learned. The results contrast with recent work and theories of second language learning that predict broad interference between L1 and L2 phonetic representations. When faced with contrasting sensorimotor demands between languages, bilinguals readily acquire and recall highly specific motor representations for speech.
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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 | 0.001 | 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".