Resolution of blur in the older eye: Neural compensation in addition to optics?
Bibliographic record
Abstract
This study examined the roles of pupillary miosis and experience-mediated compensation in older observers' superior ability to read optically blurred text. The size thresholds of younger and older adult observers for reading common words and identifying line drawings of everyday objects were compared with natural and artificial pupils as a function of systematically varied far letter acuity: best corrected, 20/30, and 20/40. With best corrected letter acuity, younger observers' size thresholds for reading words were lower than those of older observers with either natural or artificial pupils. In the 20/40 condition, however, older observers' reading size thresholds with natural pupils were significantly lower than those of the young. No significant age differences were seen at 20/30 or 20/40 on word reading with artificial pupils or on line drawing identification size thresholds with either pupil type. Prior blur experience, as estimated from observers' presenting and best optical corrections, was inversely associated with older adults' word acuity. Pupillary miosis in conjunction with neural compensation appears to account for older observers' greater ability to read blurred text.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 |
| 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".