Noise detection: Optimal summation of orientation information
Bibliographic record
Abstract
Previous work (Kersten, 1987; Taylor et al., 2003) has shown that the summation of spatial frequency information in one-dimensional noise patterns is well described by an ideal observer, indicating that human observers summate spatial frequency information optimally over a six-octave wide band. Given that many models of vision contain independent channels that only sum probabilistically across “far-apart” values of orientation as well as spatial frequency (Graham, 1989), here we investigated whether optimal summation could be extended from the dimension of spatial frequency to summation across orientation bandwidth. Observers detected Gaussian white noise with a center-frequency of 5 cycles/degree and a fixed spatial frequency bandwidth of one octave. The orientation bandwidth of the stimulus ranged from 4 degrees to 128 degrees. Six bandwidths were used and a detection threshold measured in a two-interval forced choice task for each bandwidth in both the presence and absence of a Gaussian white noise mask. Stimuli were presented for 200ms. As was found for spatial frequency, noise detection r.m.s. contrast thresholds increased with the quarter-root of the number of orientation components, consistent with the pattern of performance demonstrated by the ideal observer. Efficiency was found to be constant for bandwidths greater than 16 degrees. Currently we are investigating whether optimal summation of orientation information can be disrupted with discontinuous spectra, using the response classification technique to reveal the perceptual template for orientation summation and whether summation remains optimal as both orientation and spatial frequency bandwidth of the stimulus are increased.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".