Bibliographic record
Abstract
Detecting and integrating contours that delineate the boundaries of objects, surfaces, and other scene elements is a crucial function of what is loosely called “mid-level vision.” How are such contours and their properties processed by the brain? We here explore this question using the high-resolution Natural Scenes Dataset (Allen et al, 2021). We analyzed the BOLD activity related to eight participants viewing subsets of 73,000 images of objects and scenes within V1, V2, V3, and hV4. Using the population receptive fields of individual voxels, we sample contour properties in a spatially specific manner to construct individual regressors for each voxel. This technique, first described by Roth et al. (2022), allows us to determine to what extent voxels within the visual cortex contribute to the representation of contour properties within their receptive field across thousands of images. When analyzing the salient contours in the images, we find a strong preference for horizontal orientations, consistent with the importance for scene layout, such as the horizon line. Interestingly, this finding contrasts with a similar analysis that relies on analyzing the orientation-specific Fourier energy in the photographs, which showed a primarily radial organization of orientation preference across the visual field. We present direct comparisons of the two methods. The technique of sampling contour properties with spatial specificity opens the door to exploring a range of other contour properties, such as contour curvature, contour junctions, as well as relationships between contours, such as parallelism and symmetry. Observing the neural representations of these properties and relationships across visual regions will bring us closer to a mechanistic understanding of how our perceptual information is organized in mid-level vision.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".