Rapid Quantification of Adult and Developing Mouse Spatial Vision Using a Virtual Optomotor System
Bibliographic record
Abstract
PURPOSE: To develop a simple, rapid method of quantifying the spatial vision of mice. METHODS: A rotating cylinder covered with a vertical sine wave grating was calculated and drawn in virtual three-dimensional (3-D) space on four computer monitors facing to form a square. C57BL/6 mice standing unrestrained on a platform in the center of the square tracked the grating with reflexive head and neck movements. The spatial frequency of the grating was clamped at the viewing position by repeatedly recentering the cylinder on the head. Acuity was quantified by increasing the spatial frequency of the grating until an optomotor response could not be elicited. Contrast sensitivity was measured at spatial frequencies between 0.03 and 0.35 cyc/deg. RESULTS: Grating acuity was measurable on the day of eye opening (postnatal day [P]15: mean acuity, 0.031 cyc/deg) and reached a maximum (approximately 0.4 cyc/deg) by P24. A peak in the contrast sensitivity function emerged on P16 (4.7, or 21% contrast at 0.064 cyc/deg). The peak remained at 0.064 cyc/deg and climbed to a maximum sensitivity of 24.5, or 4% contrast, by P29. Acuity was obtained in each mouse in <10 minutes, and a detailed contrast sensitivity curve was generated in approximately 30 minutes. CONCLUSIONS: The virtual optomotor system provides a simple and precise method for rapidly quantifying mouse vision. Behavioral measures of vision in mice are essential for interpreting the results of experiments designed to reveal the cellular and molecular mechanisms of vision and visual development and for evaluating potential treatments for visual diseases.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".