Consulting the vestibular system is simply a must if you want to optimize gaze shifts
Bibliographic record
Abstract
Even simple activities like reaching for our morning cup of coffee require precisely coordinated movements of multiple parts of the body. Successive attempts at these movements are characterized by ‘repetition without repetition’ (Bernstein, 1967). For this reason, it is thought that the brain does not enforce the details of a specific movement trajectory, but rather uses on-line feedback to optimize acquisition of the movement goal. However, a study in this issue of Brain demonstrates that when we make coordinated movements of the eyes and head to redirect our gaze, we use an optimal strategy that depends on vestibular sensory input: a strategy unavailable to patients with total vestibular loss. These results provide the first evidence that the vestibular system is critical for optimizing voluntary movements (Saglam et al. , 2014). When we make coordinated eye and head movements to redirect our axis of gaze relative to space (gaze = eye-in-head + head-in-space), movement accuracy is preserved even when the head’s trajectory is experimentally altered (Cullen, 2004). This happens because within milliseconds vestibular feedback rapidly alters the motor commands to the eye and head musculature to ensure gaze accuracy (Sylvestre and Cullen, 2006). For example, when a load is transiently applied to the head during a gaze shift, both the response duration and dynamics of neurons commanding the eye movement are updated—midflight—to preserve global movement accuracy. Thus, variability across movement trajectories is not problematic because the end goal of the movement is achieved as a result of on-line vestibular feedback. However, a remaining challenge has been to develop theoretical approaches to explicitly assess whether the gaze (as well as limb; Scott, …
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".