Eyes or head: Which has the greatest effect on steering control?
Bibliographic record
Abstract
Do walkers follow their eyes or their heads? Our previous studies of goal-directed walking found that an active head turn toward a target light produced a small path deviation (4%, ~6cm) in the direction of the head turn. In contrast, an active head turn in response to a verbal cue produced a comparable deviation in the opposite direction. The former result appears to be due to attentional capture, whereas the latter may reflect a compensatory mechanism. Here we ask whether path deviations depend on the head turn, a gaze shift, or both together, and we record eye movements. To dissociate the head and eyes, we tested the following conditions during goal-directed walking: (a) active head turn towards a target light, with free eyes; (b) active head turn in response to a verbal command, with free eyes; (c) active head turn in response to a verbal command, while maintaining fixation on the locomotor goal; (d) saccade in response to a verbal command, while keeping the head facing the locomotor goal; and (e) active head turn and gaze shift in response to a verbal command. Eye movements were recorded with an ASL MobileEye tracker, and head and body movements with an Optotrak. There were three main results. First, the largest path deviation was again produced by an active head turn in the direction of a target light, with gaze free (~8cm). Second, an active head turn in response to a verbal command produced similar deviations opposite the head with or without an accompanying gaze shift. Third, an active gaze shift without an accompanying head turn did not yield any path deviations. These findings suggest that small path deviations may be due to head turns, not gaze shifts, and are largest when driven by attentional capture.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".