Bibliographic record
Abstract
What information is used by the visual system to detect patterns? A standard modelhypothesizes that both spatial frequency and orientation information are processed byindependent channels, meaning there is no summation among channels. Despite the consensus among researchers on how the visual system sums spatial frequency and orientation information, there are data in the literature (Kersten, 1987) that ostensibly contradict the standard model. To resolve this conflict, we measured the e?ciency of spatial frequency and orientation of ?ltered noise. To learn what information the visual system uses when detecting ?ltered noise, we applied a technique that can determine the information used to detect and discriminate ?ltered visual noise. In Chapter 2 the detection of spatial frequency ?ltered noise is not only e?cient but remains so with stimulus uncertainty and extremely brief (10ms) stimulus duration. When the spatial frequency channel used wasmeasured, we found a fi?xed bandwidth channel as the spatial frequency of the pattern was increased. To test the standard model, we implemented simulations of the standard model and contrary to the interpretation, the standard model could predict detection of spatial frequency ?ltered noise. Chapter 3 used spatial frequency filtered noise to relate the detection and discrimination of ?ltered visual noise. A simple rule relates what information observers use to detect and discriminate spatial frequency ?ltered noise. Chapter 4 extends the work of Chapter 2 to orientation information and found that orientation fi?ltered noise is detected efficiently. We again measured what information observers used and found that unlike SF ?filtered noise, observers use orientation in a flexible or adjustablemanner.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".