Spatial Mechanisms Mediating Visual Responses to Symmetries in Textures
Bibliographic record
Abstract
Symmetries are present at many scales in natural scenes. Humans and other animals are highly sensitive to visual symmetry, and symmetry has been shown to play a role in numerous domains of visual perception. Brain imaging studies have demonstrated that several regions in visual cortex exhibit robust and precise responses to symmetry. The current study explored the mechanisms underlying these responses, by measuring Steady-State Visual Evoked Potentials (SSVEPs) using high-density electroencephalography. Our stimuli were a class of regular textures, known as wallpaper groups: 17 unique combinations of symmetry types that represent the complete set of symmetries in 2D images. We focused on wallpaper groups PMM, which contains bilateral reflection symmetry, and P4, which contains four-fold rotation symmetry. Our SSVEP approach allows us to measure brain responses that are specific to the symmetries within each group. We measured these responses in two experiments, one (n=40) testing the influence of spatial frequency content and another (n=14) testing the influence of the repeating lattice structure that tiles the plane in all wallpaper groups. Exemplars for the spatial frequency experiment were generated based on log-domain band-limited random noise patches with center frequencies between 1 and 8 cycles-per-degree. For the lattice experiment, spatial frequency was kept constant at 2 cycles-per-degree and the ratio of the lattice to the overall wallpaper area varied between 1/12 and 1/2. Symmetry-specific responses were weaker overall for rotation compared to reflection, consistent with prior studies, but the manipulations had broadly similar effects for both: Responses were strongest at low spatial frequencies and weakened rapidly with increasing frequencies. The lattice manipulation had less dramatic effects, but results suggest that responses are stronger at lower ratios. Responses to reflection and rotation may thus depend on a similar mechanism that is highly dependent on spatial frequency and benefits from a repeating lattice structure.
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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 | 0.001 |
| 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".