Sparsely distributed contours dominate extra-striate responses to complex scenes
Bibliographic record
Abstract
The human visual system exploits redundancy in natural scenes to derive useful information. Such redundancy is frequently associated with either contours or textures within images. In this study we use fMRI to evaluate how the total amount of contrast-energy contained in contours and textures within natural images affect responses in visual cortex. We used both the entire natural image and parts of it containing predominantly contour or texture information. We modified these natural images in order to match other image properties that are known to affect cortical responses as closely as possible. These modified natural images, i.e. pseudo-natural images, remain highly recognizable. We also used synthetic images without recognizable content but with closely matched image properties. We report that most of the primary visual cortex (V1) signal variations are explained by the total amount of contrast-energy in the images. Extra-striate visual cortex, on the other hand, is driven strongest by images containing sparsely distributed contours; independent of contrast-energy amount or recognizable image content. These results provide evidence for an initial representation of natural images in V1 based on local oriented filters. Later visual cortex (and to a modest degree V1) incorporates a facilitation of contour-based structure and suppressive interactions that effectively amplify sparse-contour information within natural images.
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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) | 0.002 | 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".