Bibliographic record
Abstract
In this paper we document a simulation of the cat visual system intended to convey four of the major differences between the human and cat visual systems. Learning about an animal's visual system is an important step in understanding how that type of animal perceives the world around them, and how they behave within it. The cat visual system, for example, has been studied extensively by neuroscientists [20, 14, 1, 6], but the results of their work are difficult to convey using traditional text displays that are common in zoos, museums, and classrooms. We achieve some of our effects using fragment shaders applied as post-processes, enabling real-time simulation in either a game engine or live video feed. We also present Catalyst, an educational game that uses our simulation to teach players about the differences between human and cat vision. The tasks in Catalyst are based on the principle of situated cognition, and require the player to switch repeatedly between the two visual systems, thus emphasizing the contrast between them. The results of a user study suggest that Catalyst is successful in stimulating the interest of the players with regard to the material being presented, making it a suitable edutainment application for classrooms, zoos, or other educational settings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.177 | 0.087 |
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".