Sensory reweighting in targeted reaching: The effects of experiencing increased movement errors
Bibliographic record
Abstract
Our central nervous system (CNS) uses both visual and proprioceptive information about the locations of our body parts to enable us to move throughout the environment and complete our daily activities. We have previously shown that the brain can change how it weights sensory cues when reaching to bimodal targets (i.e. targets defined by visual and proprioceptive cues), relying more on proprioception after experiencing greater errors (i.e. greater variability) when reaching to visual targets. In the present study, we asked if similar reweighting strategies are engaged after experiencing greater errors when reaching to proprioceptive targets. Participants reached to visual (V), proprioceptive (P; left index finger) or visual + proprioceptive (VP; seen left index finger) targets. Inaccurate endpoint visual feedback was provided on V and P reaches, such that on P reaches the seen horizontal error was greater than (three times) the actual horizontal error achieved and on V reaches the seen horizontal error was smaller than (one third) the actual horizontal error achieved. No feedback was provided on VP reaches. Comparison of reach endpoints revealed that subjects did not change their sensory weighting strategy after experiencing greater variability when reaching to P targets. Thus, the CNS does not always reweight sensory cues after experiencing increased errors, suggesting that the brain processes errors associated with proprioceptive versus visual targets in a different manner.Acknowledgments: Natural Sciences and Engineering Research Council (EKC)
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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 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".