Spatial Vision Meets Spatial Cognition: Examining the Effect of Visual Blur on Human Visually Guided Route Learning
Bibliographic record
Abstract
Visual navigation is a task that involves processing two-dimensional light patterns on the retinas to obtain knowledge of how to move through a three-dimensional environment. Therefore, modifying the basic characteristics of the two-dimensional information provided to navigators should have important and informative effects on how they navigate. Despite this, few basic research studies have examined the effects of systematically modifying the available levels of spatial visual detail on navigation performance. In this study, we tested the effects of a range of visual blur levels--approximately equivalent to various degrees of low-pass spatial frequency filtering--on participants' visually guided route-learning performance using desktop virtual renderings of the Hebb-Williams mazes. Our findings show that the function of blur and time to finish the mazes follows a sigmoidal pattern, with the inflection point around +2 D of experienced defocus. This suggests that visually guided route learning is fairly robust to blur, with the threshold level being just above the limit for legal blindness. These findings have implications for models of route learning, as well as for practical situations in which humans must navigate under conditions of blur.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".