Gaze behavior when learning to link sequential action phases in a manual task
Bibliographic record
Abstract
Most manual tasks comprise a sequence of action phases. Skill acquisition in such tasks involves a transition from reactive control, whereby motor commands for the next phase are triggered by sensory events signaling completion of the current phase, to predictive control, whereby commands for the next phase are launched in anticipation of these events. Here we investigated gaze behavior associated with such learning. Participants moved a cursor to successively acquire visual targets, as quickly as possible, by actively keeping the cursor within the target zone (hold phase) for a required duration, before moving to the next target (transport phase). Distinct visual and auditory events marked completion of each phase and, with learning, the launching of the transport phase shifted from being reactively to predictively controlled. Initially, gaze was directed to the current target throughout the hold phase, allowing visual feedback control of the cursor position, and shifted to the next target in synchrony with the cursor. However, with learning, two distinct gaze behaviors emerged. Gaze either shifted to the next target well before the end of the hold phase, facilitating planning of the forthcoming cursor movement, or shifted to the next target after the cursor, enabling cursor exits to be monitored in central vision. These results suggest that, with learning, gaze behavior changes to support evolving task demands, and that people distribute different gaze behaviors across repetitions of the task.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".