Proprioceptive recalibration and uPDating predicted sensory consequences are neither exclusively implicit nor explicit
Bibliographic record
Abstract
Knowing where your limbs are is important for moving. This is informed by vision, proprioception, and prediction of sensory consequences based on efference copies. When visual feedback of hand is rotated during training, proprioception and prediction are adjusted towards the visual feedback. Here we test whether these changes are mainly implicit, hence decreasing with increased explicit information. All participants trained with a 30-degree rotated hand-cursor, and we manipulated explicit learning in three groups: 1) an group given a strategy to counter the rotation, 2) a cursor-jump group that saw the cursor jump from 0-degree to 30-degree rotation on every trial, 3) an control group, that received neither instructions nor different stimuli. During training, the instructed group countered the rotation immediately, while the other groups took longer (within 15-20 trials) to compensate for the cursor-rotation. Moreover, when including or excluding the strategy learned to counter the rotation, only the implicit group could not switch their strategy on or off at will, suggesting unawareness of their learning. Participants also localized their hand before and after training. They either moved their own hand, allowing hand localization with proprioception and efference-based predictions, or the robot moved their hand, providing only proprioceptive hand position. We found no differences between groups in either recalibrated proprioception or updated predictions. Since manipulating explicit learning clearly worked, our localization data suggests that neither proprioceptive recalibration nor updating of predictions fall on one side of the implicit-explicit learning divide, but are separate processes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".