Mechanism independence for texture-modulation detection is consistent with a filter-rectify-filter mechanism
Bibliographic record
Abstract
The ability of the visual system to detect stimuli that vary along dimensions other than luminance or color--"second-order" stimuli--has been of considerable interest in recent years. An important unresolved issue is whether different types of second-order stimuli are detected by a single, all purpose, mechanism, or by mechanisms that are specific to stimulus type. Using a conventional psychophysical paradigm, we show that for a class of second-order stimuli--textures sinusoidally modulated in orientation (OM), spatial frequency (FM), and contrast (CM)--the human visual system employs mechanisms that are selective to stimulus type. Whereas the addition of a subthreshold mask to a test pattern of the same stimulus type was found to facilitate the detection of the test, no facilitation was observed when mask and test were of different types, suggesting mechanism independence for the different types of stimulus. This finding raises the important question of whether mechanism independence is compatible with the well-known filter-rectify-filter (FRF) model of second-order stimulus detection, since FRF mechanisms, in principle, do not discriminate between stimulus types. We show that for all mask/test combinations except those with CM masks, the FRF mechanism giving the largest response to the test modulation is largely unaffected by subthreshold levels of a different stimulus-type mask. For this reason, we cannot rule out the possibility that FRF mechanisms mediate the detection of our stimuli. For combinations involving CM masks, however, we propose that a process of contrast normalization renders the test stimulus insensitive to the mask stimulus.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".