Learning to use vestibular sense for spatial updating is context dependent
Bibliographic record
Abstract
As we move, perceptual stability is crucial to successfully interact with our environment. Notably, the brain must update the locations of objects in space using extra-retinal signals. The vestibular system is a strong candidate as a source of information for spatial updating as it senses head motion. The ability to use this cue is not innate but must be learned. To date, the mechanisms of vestibular spatial updating generalization are unknown or at least controversial. In this paper we examine generalization patterns within and between different conditions of vestibular spatial updating. Participants were asked to update the position of a remembered target following (offline) or during (online) passive body rotation. After being trained on a single spatial target position within a given task, we tested generalization of performance for different spatial targets and an unpracticed spatial updating task. The results demonstrated different patterns of generalization across the workspace depending on the task. Further, no transfer was observed from the practiced to the unpracticed task. We found that the type of mechanism involved during learning governs generalization. These findings provide new knowledge about how the brain uses vestibular information to preserve its spatial updating ability.
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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 | 0.002 |
| 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".