Investigating implicit sensorimotor adaptation in a bimanual aiming task
Bibliographic record
Abstract
Sensorimotor adaptation is the process by which the brain adapts to our constantly changing environment and consists of both explicit and implicit processes. Recent models of implicit sensorimotor adaptation have focused on the realignment of the sensed hand position (Tsay et al., 2022). However, in many situations individuals must adapt to environments that do not require the control of the hand in space, but instead require the manipulation of an object or tool with both hands. Previous work has demonstrated that in a visuomotor rotation protocol that used a bimanual aiming task, there are both explicit and implicit components to the overall adaptation (Eschelmuller et al., 2023). The purpose of this project was to examine implicit sensorimotor adaptation during a bimanual aiming task, which required the control of an integrated cursor. The cursor position was determined by the angle of each elbow but did not directly correspond to the actual hand position. Participants had to move this cursor to a series of targets during error clamped visual feedback, which is thought to investigate only the implicit component of sensorimotor adaptation. Our results indicate that participants started adapting their movements away from the clamped visual feedback, reflecting implicit adaptation to the error clamp. In this task the actual hand position does not align directly with the cursor and therefore, these data indicate that the realignment process may not be directly coupled to the actual position of the hand, but instead involves a realignment of an expected end-effector position.
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 | 0.003 |
| 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 | 0.001 |
| 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".