From spatial frequency contrast to edge preponderance: the differential modulation of early visual evoked potentials by natural scene stimuli
Bibliographic record
Abstract
The contrast response function of early visual evoked potentials elicited by sinusoidal gratings is known to exhibit characteristic potentials closely associated with the processes of parvocellular and magnocellular pathways. Specifically, the N1 component has been linked with parvocellular processes, while the P1 component has been linked with magnocellular processes. However, little is known regarding the response properties of the N1 and P1 components during the processing and encoding of complex (i.e., broadband) stimuli such as natural scenes. Here, we examine how established physical characteristics of natural scene imagery modulate the N1 and P1 components in humans by providing a systematic investigation of component modulation as visual stimuli are gradually built up from simple sinusoidal gratings to highly complex natural scene imagery. The results suggest that the relative dominance in signal output of the N1 and P1 components is dependent on spatial frequency (SF) luminance contrast for simple stimuli up to natural scene imagery possessing few edges. However, such a dependency shifts to a dominant N1 signal for natural scenes possessing abundant edge content and operates independently of SF luminance contrast.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".