Periodic motion trajectory detection: Effects of frequency and radius
Bibliographic record
Abstract
Static radial frequency (RF) patterns (Wilkinson et al., Vision Research, 1998) have proved a useful tool for studying the combination of local and global processes underlying shape discrimination. Or et al.’s (JOV, 2011) investigation into detection sensitivity for temporal RF defined motion trajectories (RF 2 – 5) yielded a similar pattern of sensitivity increasing with radial frequency over this range, but with higher thresholds overall. The current study extended our investigation of parallels between spatial contour and temporal trajectory analysis by examining detection thresholds for higher RF trajectories and for a range of trajectory radii. Amplitude thresholds for detection of non-circular trajectories were measured in a 2IFC paradigm (circular vs. RF trajectory) using the method of constant stimuli. In Exp 1, thresholds for RF3, RF6, RF9, and RF12 were assessed for trajectories of 1 deg radius in 8 observers. Thresholds for the detection of RF 3 trajectories were significantly higher than thresholds for RF 6-12 [F(3,21) = 65.36, p<0.0001], which approached an asymptotic value of 0.4 min of arc. In Exp 2 (N=4), radii of 2° and 4° were examined for the same RF range. Detection thresholds increased with trajectory radius at all RFs tested. Described as a proportion of the radius (Weber fraction), thresholds were very similar across a 4-fold range of radii, suggesting trajectory shape constancy at threshold. Our findings replicate the results of Or et al. (2011) at RF3, and demonstrate that, as is the case for static RF patterns, thresholds for trajectories approach asymptote for RFs between 6 and 12 cycles, and show a constant Weber fraction for radii up to at least 4°. Although overall thresholds are higher by a factor of 2-6 than for static RF patterns, these similarities suggest that analogous global processing mechanisms may exist in the spatial and spatio-temporal domains. Meeting abstract presented at VSS 2012
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".