Processing of coarse and fine shape features by humans and deep networks: A shape frequency analysis
Bibliographic record
Abstract
BACKGROUND. The statistics of coarse and fine shape features can be analyzed using a Fourier descriptor projection. Natural shapes are known to be lowpass: coarse shape features (low shape frequencies) typically have higher amplitudes than fine shape features (high frequencies). Prior work suggests that human shape sensitivity is even more biased toward low shape frequencies than is optimal for natural lowpass shapes, however this was demonstrated only for a simple binary shape discrimination task within a linear classification framework. Deep networks are reported to be more sensitive to local shape features, suggesting a high-frequency bias, but these demonstrations have primarily been on simple artificial stimuli. Here we employ a novel Fourier method to assess the processing of coarse and fine shape features of natural shapes by humans and deep networks, in a more realistic object classification task. METHOD. Human observers (n = 11) classified frequency-filtered animal silhouettes into one of nine animal categories. To assess sensitivity to shape frequencies, the stimuli were high-pass filtered to progressively remove the lowest shape frequencies, with cutoffs ranging from the 2nd to 8th harmonic. Two representative deep networks were also evaluated on the same stimuli: a convolutional network (ResNet-50) and a transformer network (ViT). RESULTS. Both human and deep network performance declined rapidly as low shape frequencies were progressively eliminated. Trial-by-trial analysis revealed that ViT is more predictive of human responses than ResNet-50. Interestingly, the proportion of explainable human variance accounted for by ViT increased from 29% to 57% as more of the low frequencies were eliminated, suggesting that while this transformer model captures some aspects of human selectivity for higher shape frequencies, it struggles to account for human processing of the lower shape frequencies that largely determine human shape judgements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".