Task-Dependent Constraints in Motor Control: Pinhole Goggles Make the Head Move Like an Eye
Bibliographic record
Abstract
In the 19th century, Donders observed that only one three-dimensional eye orientation is used for each gaze direction. Listing's law further specifies that the full set of eye orientation vectors forms a plane, whereas the equivalent Donders' law for the head, the Fick strategy, specifies a twisted two-dimensional range. Surprisingly, despite considerable research and speculation, the biological reasons for choosing one such range over another remain obscure. In the current study, human subjects performed head-free gaze shifts between visual targets while wearing pinhole goggles. During fixations, the head orientation range still obeyed Donders' law, but in most subjects, it immediately changed from the twisted Fick-like range to a flattened Listing-like range. Further controls showed that this was not attributable to loss of binocular vision or increased range of head motion, nor was it attributable to blocked peripheral vision; when subjects pointed a helmet-mounted laser toward targets (a task with goggle-like motor demands but normal vision), the head followed Listing's law even more closely. Donders' law of the head only broke down (in favor of a "minimum-rotation strategy") when head motion was dissociated from gaze. These behaviors could not be modeled using current "Donders' operators" but were readily simulated nonholonomically, i.e., by modulating head velocity commands as a function of position and task. We conclude that the gaze control system uses such velocity rules to shape Donders' law on a moment-to-moment basis, not primarily to satisfy perceptual or anatomic demands, but rather for motor optimization; the Fick strategy optimizes the role of the head as a platform for eye movement, whereas Listing's law optimizes rapid control of the eye (or head) as a gaze pointer.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".