Bibliographic record
Abstract
We find that adaptation to twinkle or flicker slows down the perceived rate of twinkle, flicker, and motion. Twinkle adaptation. Observers adapted to a field, 11° wide x 22° high, of twinkling dynamic random noise, namely black/white random dots (6 minarc diam) that refreshed with spatially uncorrelated dots 27 times per second. They then viewed random test dots that twinkled at rates between 8 and 27 fps on different trials. Result: a matching method showed that the test dots now appeared to twinkle at half their actual rate. Observers also adapted to a random-dot pattern that flickered in counterphase, alternating between its own positive and negative 27 times per sec (13.5 Hz). After this adaptation, a fresh field of test dots that counterphased at 4 to 13.5 Hz appeared to flicker at about half its actual rate. We attribute these results to visual filters tuned to different temporal frequencies, with spatial resolution able to resolve the fine-grain random dots. Flicker adaptation. Following adaptation to a spatially uniform rectangle that flickered between black and white at 13.5 Hz, a congruent uniform test rectangle flickering at 4 to 13.5 Hz appeared to fall to 70% of its actual flicker rate. We attribute these results also to temporally tuned visual filters, but of undetermined spatial resolution. Cross-adaptation. Adapting to 27 fps twinkle caused test random dots that drifted at speeds between 0.25°/s and 3°/s to slow down perceptually to 60--80% of their actual speed. Possibly, twinkle contains apparent movement in all directions that adapt all motion sensors. Similarly, adapting to 13.5Hz spatially uniform flicker also reduced perceived test speeds to 80% of their actual speed. This implies that prolonged viewing of flicker leads to adaptation of the temporal, not spatial, components of the test motion. Meeting abstract presented at VSS 2013
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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 | 0.000 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".