Different spatial frequency bands selectively signal for natural image statistics in the early visual system
Bibliographic record
Abstract
Early visual evoked potentials (VEPs) measured in humans have recently been observed to be modulated by the image statistics of natural scene imagery. Specifically, the early VEP is dominated by a strong positivity when participants view minimally complex natural scene imagery, with the magnitude of that component being modulated by luminance contrast differences across spatial frequency (i.e., the slope of the amplitude spectrum). For scenes high in structural complexity, the early VEP is dominated by a prominent negativity that exhibits little dependency on luminance contrast. However, since natural scene imagery is broad band in terms of spatial frequency, it is not known whether the above-mentioned modulation results from a complex interaction within or between the early neural processes tuned to different bands of spatial frequency. Here, we sought to address this question by measuring early VEPs (specifically, the C1, P1, and N1 components) while human participants viewed natural scene imagery that was filtered to contain specific bands of spatial frequency information. The results show that the C1 component is largely unmodulated by the luminance statistics of natural scene imagery (being only measurable when such stimuli were made to contain high spatial frequencies). The P1 and N1, on the other hand, were observed to exhibit strong spatial frequency-dependent modulation to the luminance statistics of natural scene imagery. The results therefore suggest that the dependency of early VEPs on natural image statistics results from an interaction between the early neural processes tuned to different bands of spatial frequency.
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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 | 0.002 |
| 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) | 0.002 | 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".