The Relationship Between Blink Rate and Navigation Task Performance
Bibliographic record
Abstract
Emerging research has suggested a correspondence between eye-blink rate and task performance. Recently, Tsai, Viirre, Strychacz, Chase, & Jung (Aviation, Space, and Environmental Medicine, 2007) demonstrated an increase in blink rate as a function of split attention during a basic driving task. However, the relationship between blink rate and navigation performance in a more complex navigation task requires further investigation. The present study investigated the relationship between blink rate and navigation performance as a function of the complexity of an environment using a two-step navigation task in virtual reality. Participants were asked to navigate through two novel virtual environments to a central landmark, and then were asked to navigate back to the starting position. The two environments consisted of identical buildings, but differed in their arrangement within the environment, resulting in a high and low intelligibility environment. Additionally, the influence of textural information was manipulated between subjects by providing either unique or uniform textures for each building within the environments. An analysis of variance on the overall movement paths, duration of navigation, and blink rate for both the exploratory and wayfinding tasks revealed a significant main effect of configuration on the distance, duration, idiosyncracy of each path, and blink rate of each participant. Critically, this effect was observed during the wayfinding task, but not the exploration task, with a higher blink rate and longer movement paths being observed in the low intelligibility environment relative to the high intelligibility environment. Only limited evidence was found for the influence of textural information on these results. Taken as a whole, these results provide early evidence for the differential allocation of attention during navigation through complex environments, resulting in reduced navigation performance.
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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 | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".