Local Symmetry in Human and Artificial Neural Networks
Bibliographic record
Abstract
We can rapidly identify the image of a scene as a beach, a forest, or a highway. This ability relies in part on perceptual grouping cues. Interestingly, past studies found that both the human visual system (HVS) and convolutional neural networks (CNNs) are sensitive to and benefit from perceptual grouping cues such as local symmetry in scenes. Yet, we still do not know exactly how local symmetry facilitates scene categorization and whether HVS and CNNs use the cue in a similar manner. In the present study, we explore this question with representational similarity analysis (RSA), in which we compare the scene representations of the HVS with those of the CNN VGG16. Specifically, for the HVS, we computed representational dissimilarity matrices (RDMs) for ten regions of interest (ROIs) in the visual cortex using the BOLD5000 dataset. For VGG16, we created an RDM for each convolutional layer. Subsequently, we measured correlations between the RMDs for the ROIs and VGG16 layers. Moreover, we correlated all RDMs to a symmetry dissimilarity matrix (SDM) based upon the local symmetry in each scene. Consistent with previous results, we found that half of the participants had high correlations between the RDMs and SDM for low-level visual areas (e.g., V1). However, half showed high correlations for mid- to high-level areas (V4 and RSC), suggesting some variability among observers. We also found that later layers of VGG16 exhibited stronger associations with the SDM than earlier layers. We expected such a finding on a feed-forward network like VGG16 because local symmetry inherently involves longer-range relationships, which are present in higher layers due to their large receptive fields. To conclude, although local symmetry influences both the HVS and VGG16, the two systems process this cue differently, likely due to architectural limitations of feed-forward neural networks.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".