Perceptual limits on 2D motion-field visualization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper examines perceptual issues in 2D motion-field visualization. Several aspects of motion-field perception are considered, including sensitivity to spatial gradients, number of motion layers, and motion blur. Our analysis concentrates on a specific popular method for 2D flow visualization, namely, line integral convolution (LIC). Using 2D spectral analysis, we examine a tradeoff that arises in dynamic LIC between the static motion blur cue, which indicates motion direction and the dynamic cue which indicates image speed. We also present a 2D spectral synthesis method for motion-field visualization, along with several examples. The synthesis method is simple to implement and to analyze in the frequency domain, which makes it a convenient tool for studying the perception of complex motion fields.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it