Sensory reweighting following exposure to misaligned endpoint error feedback
Bibliographic record
Abstract
When an object's location is defined by redundant sensory information, the brain combines these sensory inputs to form a coherent estimate of the object's location in such a way that the more reliable sensory cue is assigned a greater weight (Ernst and Banks, 2002). In the present study we asked if the motor system can learn to change the way in which it integrates sensory cues. Participants reached to visual (V), proprioceptive (P; their left index finger) or visual + proprioceptive (VP; their seen left index finger) targets. Inaccurate endpoint visual feedback was provided on the V and P reaches, such that on V reaches the seen horizontal error was greater than (three times) the actual horizontal error achieved and on P reaches the seen horizontal error was smaller than (one third) the actual horizontal error achieved. No feedback was provided on VP reaches, which were completed before and after reaching to V and P targets. To determine the weight assigned to V and P cues when reaching to VP targets, we compared reach endpoints on VP reaches with reach endpoints achieved on V and P reaches. Results indicate that after experiencing misaligned endpoint error feedback, participants adjusted their reaches to VP targets such that their endpoints resembled those achieved on P reaches. These results suggest that the brain can change how it weights sensory information regarding target location, relying more on the sensory modality which has been experienced as most accurate.Acknowledgments: Research support: Natural Sciences and Engineering Research Council (EKC)
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 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".