Improved discrimination of visual sensory prediction errors with tendon vibration
Bibliographic record
Abstract
Implicit sensorimotor adaptation is driven by the difference between the expected and actual sensory consequences of a movement, known as a sensory prediction error (SPE). In a visuomotor rotation task, adaptation is assumed to be driven by the SPE between the rotated visual feedback and the sensory prediction based on the intended action. Via adaptation, movements shift away from the target in the opposite direction of the perturbation, thus reducing the SPE based on visual feedback. However, as a result of adaptation, an SPE resulting from the mismatch between the expected and actual proprioceptive feedback increases. Thus, one constraint on the extent of adaptation to the visual rotation may lie in how visual SPEs interact with proprioceptive SPEs (Morehead et al., 2017). As a starting point, we asked how the perceived trajectory of self-selected reaching movements is affected by the addition of proprioceptive noise. Participants (10) performed rapid reaching movements and judged whether visual cursor feedback representing fingertip position was rotated clockwise or counterclockwise with respect to their reach (Synofzik et al., 2010). We used simultaneous biceps-triceps tendon vibration during the movement to implement proprioceptive noise (Bock & Thomas, 2011). We derived estimates of discrimination sensitivity (JND) under conditions of tendon vibration and no vibration. Contrary to our expectations, all participants' JNDs improved with concurrent vibration (8.2 deg) compared to no vibration (16.5 deg). We speculate that the presence of proprioceptive noise may increase the weight given to the visual prediction, resulting in the paradoxical improvement of the JND.Acknowledgments: Supported by the Natural Sciences and Engineering Research Council of Canada (NSERC).
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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) | 0.000 | 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) | 0.002 | 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".