Evidence for a reference frame transformation of vestibular signal contributions to voluntary reaching
Bibliographic record
Abstract
To contribute appropriately to voluntary reaching during body motion, vestibular signals must be transformed from a head-centered to a body-centered reference frame. We quantitatively investigated the evidence for this transformation during online reach execution by using galvanic vestibular stimulation (GVS) to simulate rotation about a head-fixed, roughly naso-occipital axis as human subjects made planar reaching movements to a remembered location with their head in different orientations. If vestibular signals that contribute to reach execution have been transformed from a head-centered to a body-centered reference frame, the same stimulation should be interpreted as body tilt with the head upright but as vertical-axis rotation with the head inclined forward. Consequently, GVS should perturb reach trajectories in a head-orientation-dependent way. Consistent with this prediction, GVS applied during reach execution induced trajectory deviations that were significantly larger with the head forward compared with upright. Only with the head forward were trajectories consistently deviated in opposite directions for rightward versus leftward simulated rotation, as appropriate to compensate for body vertical-axis rotation. These results demonstrate that vestibular signals contributing to online reach execution have indeed been transformed from a head-centered to a body-centered reference frame. Reach deviation amplitudes were comparable to those predicted for ideal compensation for body rotation using a biomechanical limb model. Finally, by comparing the effects of application of GVS during reach execution versus prior to reach onset we also provide evidence that spatially transformed vestibular signals contribute to at least partially distinct compensation mechanisms for body motion during reach planning versus execution.
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".