Bibliographic record
Abstract
Purpose. It is now well accepted that the early stages of visual processing comprise mechanisms that are relatively narrowband for spatial frequency (1 octave) and orientation (30°). It is less clear whether the outputs of these narrowband mechanisms can be individually accessed by later stages of perception. We address this question using elementary, local motion and stereo tasks. Methods Our stimulus comprised a disc containing fractal noise embedded in a field of fractal noise. The fractal noise in the disc was spatially displaced between eyes/frames resulting in either a near/far disparity task or a left/right motion task. The noise was stochastically filtered (amplitudes unaltered, just phases scrambled outside passband) using idealized filters of variable bandwidth and peak spatial frequency. In this way a band of correlated information was preserved with uncorrelated information at higher and lower spatial frequencies (a notched filter of signal correlation). Phase scrambling involved either spatial frequencies or orientations of noise components. We used a simple Gaussian signal/noise model to derive the minimum spectral region that subserved our tasks. Results Similar results were found for the stereo and motion tasks. In either case the minimum bandwidth necessary to accomplish these tasks was many times previous estimates of the bandwidth of early visual mechanisms. In fact it closely corresponded to the spatial frequency and orientation spectrum of the stimulus, suggesting that all stimulus information was necessary. Conclusion For both local motion and stereo, there is no individual access to information from narrowband channels tuned to either spatial frequency or orientation.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".