A hypothesis-based approach to clinical psychophysics and to the design of visual tests: the Proctor Lecture.
Bibliographic record
Abstract
Over the past 30 years or so, it has become increasingly clear that even gross defects of visual function can be hidden from detection by classic clinical tests. In this lecture, I have outlined a hypothesis of visual processing that offers a guide to the design of tests for revealing forms of visual loss that are hidden from detection by the classic tests of visual function and have given examples of the application of the hypothesis. A further point bears on the growing involvement of ophthalmologists and vision scientists in developing screening tests for individuals who must perform tasks that require high skills in visually guided motor action, such as driving a car or truck, flying a passenger aircraft, flying a medical emergency helicopter, and providing visual surveillance in air–sea rescue operations. The hypothesis can guide the design of screening tests that are specific for the task to be performed. 1–3 At one time, basic research on the detection of objects and discriminations of their shapes and configurations was restricted to luminance-defined form. However, recent findings indicate that rather than containing only one subsystem for the early processing of the spatial attributes of objects, the visual system contains five parallel subsystems, the performances of three of which compare well with that for luminance-defined form, except only for fine detail and sharp edges (reviewed in Ref. 4). In the second half of this article is discussed the implications of these findings for clinical psychophysics.
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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 | 0.023 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".