Spatial frequency streams in natural scene categorization
Bibliographic record
Abstract
We used the Bubbles method (Gosselin & Schyns, 2001) to examine the effective use of spatial frequencies through time in natural scene categorization. Two observers (C.E and K.T) categorized a total of 8640 dynamic stimuli (6 deg2 of visual angle*180ms) composed of one of 720 natural scenes from six categories (beach, city, mountain, forest, highway and landscape). Each of our stimuli was composed of 18 frames, made from the dot product of the Fourier spectrum of a scene with 2D white Gaussian noise convolved with a Gaussian function (Std's = 0.08 of the Nyquist frequency and 79 ms). We performed a linear regression on reaction times and sampling noise. The resulting classification image shows the use of different spatial frequencies across the 18 frames composing every animation. We conducted a one tailed Z-score analysis based on random field theory (Chauvin et al, submitted) in order to reveal the use of spatial frequency as a function of time. The classification image (Z > 3.8, p < 0.01) reveals the use of three narrow bands of spatial frequencies across time. Low frequencies (1 c/dg) are first to reach signifiance (between 10 and 90 ms), followed by mid frequencies (6 c/dg, significant between 30 and 120 ms) and finally higher frequencies (12 c/dg, significant between 70 to 100 ms). Superficially, this result corroborates the coarse-to-fine hypothesis (Parker, Lishman, & Hughes, 1992) of natural scenes categorization. It allows, however, a much finer analysis of the information subtending the first moments of visual categorization.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".