Modelling eye-head coordination without pre-planning — A reflex-based approach
Bibliographic record
Abstract
The gaze orientation system is a prime example of the CNS using multiple platforms to achieve its goal. To move the gaze in space, the eyes, head, and body cooperate to place the image of the target on the fovea. Understanding the underlying neural circuitry innervating this collaboration could also be a cue to understanding other movement related CNS tasks involving multiple platforms, i.e., posture and locomotion. Basically two major network topologies for modeling the gaze orientation system have been proposed: the independent controller model and the shared gaze feedback controller model. In the independent controller model, each platform (i.e., eyes, head or trunk) receives its own share of the retinal error (distance of the target from the current gaze position) independent from other platform(s) and its goal is to null its individual error, whereas, in the shared gaze feedback controller all platforms collaborate to null the shared global error, which is calculated on the fly using feedback from all platforms or reflexes. Each of the mentioned general topologies has its own supporters and the question is which does the CNS actually use. In this article, based on evidence from neurophysiology and behavior, complemented by simulation data, it will be shown why a shared feedback controller is the better candidate for this task. More specifically, simulations of an updated Prsa-Galiana model (the Shared Sensory-Motor Integration (SMI) model) will be discussed in more detail and, where applicable, compared with other popular models, including independent and shared controller models. It provides plausible explanations for observations on gaze shifts with various interventions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".