Contours, not textures determine orientation tuning in humans
Bibliographic record
Abstract
Ever since the seminal discoveries of Hubel and Wiesel, we know that cortical representations of visual input start with oriented edges and lines in the primary visual cortex. Determining exactly how orientation information is processed in the human brain is critical for understanding the computational and neural mechanisms of vision. Filter and contour-based methods, including simple bars and Gabor filters, have historically been used interchangeably for detecting orientation activity. While these approaches effectively capture orientation, filter-based methods typically aggregate data across spatial frequencies, blending texture and contour information. This overlap raises important questions about what specific orientation features are most relevant for perception and neural representation. In this study, we address this important question with two complementary approaches. First, we investigated human orientation judgments using image patches with maximal and minimal differences between average orientations computed by steerable pyramid filters and a contour-based method. Behavioural results revealed that human judgments align with contour-based orientation but not with filter-based orientation when the two were in conflict. Observers clearly prioritized contours over textures when summarizing orientation in complex scenes. Second, we evaluated the impact of orientation computation methods on neural maps of orientation selectivity, using Roth and colleagues' (2022) image-computable model as a benchmark. By comparing filter-based methods applied to photographs and line drawings with a contour-based method, we assessed how the choice of computation influences model fit and voxel-level orientation preference in the visual cortex. Again, we found a clear advantage of contours over textures in explaining orientation tuning in the visual cortex. These findings underscore the importance of oriented contours rather than textures as the elemental building blocks of vision. By highlighting the importance of contours for human orientation judgments and neural selectivity in the visual cortex, our work emphasizes the need for methodological alignment in visual neuroscience research.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".