My 50 Year Odyssey to Develop Behavioral Methods to Let Me See Quickly How Well Kittens See
Bibliographic record
Abstract
The importance of animal models to an understanding of the development and plasticity of visual functions was evident from the outset of the long experimental collaboration of David Hubel and Torsten Wiesel in the early 1960s. Their initial work on kittens had massive impact in part because of the recognition that kittens share with primates substantial similarities of visual system organization and plasticity (e.g., eye-specific lamination of the thalamus and columnar organization of the visual cortex), as well as comparable visual abilities (including stereoscopic vision). In addition the plasticity demonstrated in response to early periods of selected visual exposure provided a glimpse into the origins of amblyopia. Five decades ago my laboratory developed a method for the fast measurement of visual thresholds in kittens in order to capture the consequences for spatial vision of the rapid physiological changes that occurred in the visual cortex during both typical development and those that follow various forms of early selected visual exposure. This paper describes the further evolution of the method with an emphasis on the testing procedures that enable fast capture of spatial visual thresholds such as visual acuity on every animal and occasion. In these respects, the method emulated features of basic tests of human spatial vision as applied in clinical settings. As with clinical tests for humans, the method includes benchmarks of low vision necessary to document the profound immediate consequences of early selected visual deprivation and the speed and extent of the subsequent recovery.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.013 |
| Insufficient payload (model declined to judge) | 0.020 | 0.015 |
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".