The Effects of Optical Blur on Motion and Texture Perception
Bibliographic record
Abstract
PURPOSE: The purpose of this study is to determine how decreased visual acuity affects performance on tasks of motion and texture perception. METHODS: Positive diopter lenses were used to match three subjects at five levels of decimal visual acuity (DVA) ranging from an uncorrected DVA of 1.6 to the lowest DVA of 0.2. Performance thresholds were determined at each acuity level for five different psychophysical tasks. The tasks assessed the perception of motion-defined form, global motion, maximum motion displacement (Dmax), texture-defined form, and global texture. RESULTS: Reducing visual acuity decreased performance on the tasks of motion-defined form identification, texture-defined form identification, and global texture integration. Performance on the Dmax task improved with a reduction in visual acuity. Performance on the global motion task was unaffected by changes in visual acuity. CONCLUSIONS: Visual acuity should be considered when interpreting the results of developmental or clinical studies of motion and texture perception. The only exception to this is global motion perception, at least when DVA is better than 0.2. The effect of blur on tasks of motion and texture perception may reflect the extent to which high spatial frequency information is required for performance on these tasks.
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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".