The perils of learning to move while speaking: One-sided interference between speech and visuomotor adaptation
Bibliographic record
Abstract
Our understanding of the adaptive processes that shape sensorimotor behaviour is largely derived from studying isolated movements. Studies of visuomotor adaptation, in which participants adapt cursor movements to rotations of the cursor’s screen position, have led to prominent theories of motor control. In response to changes in visual feedback of movements, explicit (cognitive) and implicit (automatic) learning processes adapt movements to counter errors. However, movements rarely occur in isolation. The extent to which explicit and implicit processes drive sensorimotor adaptation when multiple movements occur simultaneously, as in the real world, remains unclear. Here, we address this problem in the context of speech and hand movements. Participants spoke in-time with rapid, hand-driven cursor movements. Using real-time auditory alterations of speech feedback, and visual rotations of the cursor’s screen position, we induced sensorimotor adaptation in one or both movements simultaneously. Across three experiments (n = 184), we demonstrate that visuomotor adaptation is markedly impaired by simultaneous speech adaptation, and the impairment is specific to the explicit learning process. In contrast, visuomotor adaptation had no impact on speech adaptation. The results demonstrate that the explicit learning process in visuomotor adaptation is sensitive to movements in other motor domains. They suggest that speech adaptation may lack an explicit learning process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".