Increment threshold functions for radial frequency motion trajectories exhibit a dipper function above threshold
Bibliographic record
Abstract
Radial frequency (RF) trajectories are a new class of stimuli that have been developed to study the visual perception of periodic motion (Or, Thabet, Wilkinson, and Wilson, 2011). These stimuli are described by a moving dot that traverses a circular path through space with periodic radial deformations whose frequency, amplitude, and phase can be independently specified. Here, we show how the discrimination of RF amplitude varies across different reference amplitudes in a 2 alternative forced choice task. Using an RF3 trajectory (a pattern with three cycles of deformation along its trajectory), increment thresholds for seven observers were measured at four different reference amplitudes: Detection (discriminating a circular motion from RF3), 1X (discriminating a pair of RF3 patterns, with the amplitude of one member of this pair set to (1X) threshold obtained from the detection condition), 2.5X, and 5X. Data show that thresholds for detecting changes in amplitude show an approximately two-fold decrease at 1X and 2.5X, relative to detection threshold, and then recover to detection threshold levels at 5X. Mean thresholds (± standard errors) for detection, 1X, 2.5X, and 5X (in minutes of arc): 2.30 ± 0.31, 1.21 ± 0.18, 1.06 ± 0.12, 2.31 ± 0.42, respectively. A repeated measures ANOVA showed a main effect of base increment: F(1,6) = 95.80, p<0.0001 ); paired sample t-tests showed a significant difference between detection and 1X, T(6) = 3.59 (p<0.05); detection and 2.5X, T(6) = 4.28 (p<0.01); and no difference between detection and 5X, T(6) = -0.03 (p = 0.98). Observers were also tested using an RF5 trajectory, and the same pattern was found. As a control, this effect was measured using different angular speeds of the RF trajectory. We conclude that the discrimination of RF trajectories along different base amplitudes points to a sigmoidal neural response function for deviations from circular trajectories. 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".