The effects of optically and digitally simulated aniseikonia on stereopsis
Bibliographic record
Abstract
PURPOSE: To simulate both lens-induced and screen-induced aniseikonia, and to assess its influence on stereopsis. Additionally, to determine if screen-based size differences could neutralise the effects of lens-induced aniseikonia. METHOD: A four-circle (4-C) paradigm was developed, where one circle appears in front or behind the others because of crossed or uncrossed disparity. This stereotest was used for three investigations: (1) Comparison with the McGill modified random dot stereogram (RDS), with anisometropia introduced with +2 D spheres and cylinders, and with aniseikonia introduced with 6% overall and 6% meridional (×180, ×90) magnifiers before the right eye; (2) Comparison of lens-induced and screen-induced 6% overall and meridional magnifications and (3) Determining if lens and screen effects neutralised, by opposing 6% lens-induced magnification to the right eye with screen-inducements of either 6% left eye magnification or 6% right eye minification. A pilot study of the effect of masking versus not masking the surround was also conducted. RESULTS: The 4-C test gave higher stereo-thresholds than the RDS test by 0.5 ± 0.2 log units across both anisometropic and aniseikonic conditions. However, variations in power, meridian and magnification affected the two tests similarly. The pilot study indicated that surround masking improved neutralisation of screen and lens effects. With masking, lens-induced and screen-induced magnifications increased stereo-thresholds similarly. With lens and screen effects opposed, for most participants stereo-thresholds returned to baseline for overall and ×180 magnifications, but not for ×90 magnification. Only three of seven participants showed good compensation for ×90 magnification. CONCLUSIONS: Effects of lens-induced aniseikonia on stereopsis cannot always be successfully simulated with a screen-based method. The ability to neutralise refractive aniseikonia using a computer-based method, which is the basis of digital clinical measurement, was reasonably successful for overall and ×180 meridional aniseikonia, but not very successful for ×90 aniseikonia.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".