Detection of radial frequency motion trajectories
Bibliographic record
Abstract
Humans are extremely sensitive to radial deformations of static circular contours (Wilkinson, Wilson, & Habak, 1998, Vision Research). Here we investigate the detection of motion trajectories defined by these radial frequency (RF) patterns over a range of radial frequencies, using the method of constant stimuli combined with a two-interval forced-choice (2IFC) paradigm. The stimulus was a radially symmetric difference-of-Gaussians blob (peak spatial frequency: 2.74 cpd, bandwidth: 1.79 octaves at half amplitude) moving around the trajectory defined by an invisible RF pattern (motion RF), or by a circle of equivalent mean radius, for one complete revolution. The observer's task was to identify the interval containing the motion RF as a function of deformation amplitude; threshold was defined as 75% correct performance. Radial frequencies of 2 – 5 cycles were tested at a mean radius of 1.0 arc deg and a mean rotation speed of 3.14 arc deg/s (2.0 s for a complete revolution). Detection thresholds ranged from 0.8 – 4.6 arc min and followed a power function with an average exponent of −1.48 as a function of radial frequency. This decreasing trend was consistent with that found in static RFs, although detection thresholds for motion RFs were significantly higher (0.2 – 0.5 arc min for static RFs). Whether the sensitivity to motion RFs is dependent on local cues or global shape is currently under investigation. Importantly, we showed that these novel stimuli should be a useful tool to investigate trajectory learning and discrimination.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".