Contrast-modulated stimuli detection is unaffected by luminance-modulated noise
Bibliographic record
Abstract
In a previous study, we have shown that the sensitivity difference between luminance- (LM) and contrast-modulated (CM) stimuli results from a difference of internal equivalent noise and not from a difference of calculation efficiency. The objective of the present study was to seek the source of the internal noise limiting the sensitivity to CM stimuli. Three types of noise were used: band-pass LM noise near the carrier (LMN-carrier) or envelope (LMN-envelope) spatial frequency and band-pass CM noise near the envelope spatial frequency (CMN-envelope). For the five observers, the noise contrasts were adjusted to increase the detection thresholds by 0.5 log units for their respective stimuli: carrier, LM and CM. LM and CM detection thresholds were subsequently evaluated in these three noise conditions using a constant stimuli paradigm. As expected, LMN- and CMN-envelope increased the detection threshold of their respective stimulus. However, no cross-type interactions were found: LMN- and CMN-envelope had no significant impact on CM and LM stimuli detection respectively, and LMN-carrier did not affect the detection thresholds of LM and CM stimuli. This double dissociation is strong evidence suggesting that both stimuli are processed, at least partially, by separate mechanisms and that they are not merged after a second-order rectification applied to CM stimuli. The results also suggest that, in the tested conditions, pre-rectification noise affecting the carrier visibility is not a limiting factor for CM stimuli sensitivity.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".