Discrimination and identification of periodic motion trajectories
Bibliographic record
Abstract
Humans are extremely sensitive to radial deformations of static circular contours (F. Wilkinson, H. R. Wilson, & C. Habak, 1998). Here, we investigate detection and identification of periodic motion trajectories defined by these radial frequency (RF) patterns over a range of radial frequencies of 2-5 cycles. We showed that the average detection thresholds for RF trajectories range from 1 to 4 min of arc and performance improves as a power-law function of radial frequency. RF trajectories are also detected for a range of speeds. We also showed that spatiotemporal global processing is involved in trajectory detection, as improvement in detection performance with increasing radial deformation displayed cannot be accounted for by local probability summation. Finally, identification of RF trajectories is possible over this RF range. Overall thresholds are about 6 times higher than previously reported for static stimuli. These novel stimuli should be a useful tool to investigate motion trajectory learning and discrimination in humans and other primates.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".