Evidence for an efferent-based prediction contributing to implicit motor adaptation
Bibliographic record
Abstract
Abstract Models of sensorimotor adaptation have proposed that implicit adaptation is driven by error signals created by discrepancies between various sensory information sources. While proprioception has been suggested as a critical source for the error signals driving adaptation, the role of an efferent-based motor prediction has largely been neglected. In this study, we examined the effect of dissociating the afferent and efferent information available during implicit adaptation. Participants moved a visual cursor towards targets by applying horizontal forces to a stationary handle at a central home location. During perturbation trials, the cursor followed an invariant path rotated relative to the target. Participants were instructed to ignore this task-irrelevant cursor feedback and to isometrically “reach” towards the target. Participants implicitly adapted in the isometric task, even when the hand never actually moved to the target. Moreover, the level of adaptation surpassed that of a typical clamped reaching paradigm by nearly twofold. This was confirmed in a secondary experiment where participants performed actual reaching movements and demonstrated significantly less adaptation. Our findings suggest that while afferent proprioceptive feedback of hand position around the target most likely plays a role in adaptation, it is not necessary to induce adaptation.
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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 | 0.004 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".