Simulating visual systems using npr techniques: methodology, framework, and case studies
Bibliographic record
Abstract
In this dissertation I examine how research in non-photorealistic rendering, human perception, and game-based learning can be combined to produce illustrative simulations of different visual systems that effectively convey information about vision to unprimed observers. The Visual Differences Simulation (VDS) methodology and framework that I propose is capable of producing simulations of animal visual systems based on how they relate to human vision, and can represent differences in color vision, hyperspectral sensitivity, visual acuity, light sensitivity, field of view, motion sensitivity, and eye construction. The simulations produced by the VDS framework run in real time, allowing users to explore computer-generated environments from 'behind the eyes' of an animal in an interactive and immersive manner. I also examine how cognitive principles and game-based learning can be leveraged to demonstrate and enhance the educational impact of the simulations produced by the VDS frame- work. Two case studies are presented, where simulations of the cat and the bee visual systems are used as the basis to design educational games, and are evaluated to show that embedding the simulations in educational games is an effective and engaging way to convey information about vision to unprimed observers.
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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 | 0.003 | 0.006 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".