Brain Responses to Symmetries in Naturalistic Novel Three-Dimensional Objects
Bibliographic record
Abstract
Symmetries are prevalent in natural and man-made objects and scenes. During natural vision, symmetries in the world are subject to perspective-distortion and rarely produce symmetrical images on the retina. Here we used high-density EEG to investigate the ability of the human visual system to overcome such distortions, by measuring responses to images of naturalistic, novel, 3D objects. Our paradigm used Steady-State Visual Evoked Potentials (SSVEPs) to isolate brain activity specific to symmetry processing. We presented images of symmetrical and asymmetrical objects under two viewing conditions: One that produced symmetries in the image plane, and another where objects were rotated such that symmetries would be distorted in the resulting image. In each stimulus cycle, an asymmetrical object image was shown followed by a second image, either symmetrical or, in a control condition, another asymmetrical image. Image pairs for each cycle were selected so activity in a deep convolutional neural network trained to classify object categories (VGG16; Simonyan and Zisserman, 2015) was similarly matched between all image-pairs. We showed 10 cycles per trial, at a stimulation frequency of 1 Hz. In a follow-up experiment, the shading cues to 3D shape were removed from the images. In the resulting 2D object silhouettes, perspective-distorted symmetry cannot be detected. We analyzed the SSVEP data in three electrode regions-of-interest over occipital cortex and left and right temporal cortex, defined based on independent data. The results showed that during passive viewing, perspective-distorted symmetry can elicit measurable symmetry-specific SSVEPs, but compared to image-plane symmetry they are weaker, more anterior and possibly more right-lateralized, consistent with responses in higher-level visual cortex. The 2D stimuli elicited similar responses to 3D for image-plane symmetry, but as expected, produced no symmetry responses for perspective-distorted symmetry. Future work will determine how task manipulations may influence responses to these stimuli.
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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) | 0.001 | 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".