Noise detection: Summation of high spatial frequency 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, suggesting that human observers may sum spatial frequency information optimally over a six-octave wide band. This result is surprising, given that many models of vision contain independent channels that only sum probabilistically (Graham, 1989). However, the previous studies used a restricted set of conditions; specifically, the center frequency was at or near the peak of the CSF. To further test the idea of optimal summation, we had observers perform a detection task with band-pass Gaussian white noise centered at 15 cycles/degree. Spatial frequency bandwidth varied from one-half to four octaves. Stimuli were presented for 200ms in a two interval forced-choice task. Unlike what was found for noises centered at 5 cycles/degree, we found that detection thresholds were not consistent with ideal frequency summation. Hence, the ideal frequency summation reported previously does not generalize to other bands of spatial frequency. Currently we are using the response classification technique to reveal the perceptual template for the detection of these patterns. We will discuss how the frequency summation data can be explained using a standard multiple-channel model (Wilson & Gelb, 1984).
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".