Testing Senders’ Visual Occlusion Model: Do Operators (Drivers) Really Predict During Visual Occlusion?
Bibliographic record
Abstract
OBJECTIVE: The present study tests the hypothesis that humans are capable of predicting the state of a system during visual occlusion, an assumption often made in models of sampling behaviour, but seldom tested. BACKGROUND: In 1967, John Senders introduced the visual occlusion paradigm to evaluate attentional demand of tasks such as automobile driving. Despite multiple studies employing this paradigm, the concept of operators actually being able to resolve uncertainty during occlusion by predicting system output has remained unvalidated. METHOD: A self-paced visual occlusion monitoring task was contrived, involving a randomly rotating basin with a ball at the bottom. Participants were required to detect critical events (ball falling off the edge) while looking only as often as subjectively deemed necessary. Assuming the need to resolve uncertainty imposed by the random rotations, we examined relations between occlusion durations and system states preceding occlusion, for different glance durations, to infer whether predicting may have taken place. RESULTS: Results suggested that glance requests were consistent with the use of simple first order predictions. This pertained not only for longer (300 and 500 ms) glances, but even for 100 ms glances whenever critical events were imminent. CONCLUSION: The presumption that human operators are capable, under certain circumstances, of predicting system state in the absence of visual information appears feasible; however, glance duration plays an important role. APPLICATIONS: By providing support for some of its basic premises, the use of Senders' visual occlusion paradigm as a potential tool for evaluating human monitoring performance has been strengthened.
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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 | 0.005 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".