Differential sensitivity of humans and deep networks to the amplitude and phase of shape features
Bibliographic record
Abstract
Background: While humans are highly sensitive to global shape information, deep neural networks models (DNNs) trained on ImageNet seem to favor local shape features. In the Fourier descriptor (shape frequency) domain, this manifests as much higher human sensitivity to low shape frequencies. Here we ask how this differential sensitivity depends upon the amplitude vs phase structure of these Fourier shape components. Methods: Human observers (n=68) classified animal silhouettes into nine categories. The shapes were lowpass filtered in the shape frequency domain, over a range of frequency cutoffs, using two filtering methods. In method 1, Fourier components beyond the cutoff were zeroed. In method 2, phases were randomized but amplitudes were preserved. We compared human performance against three representative networks: a convolutional model (ResNet-50) and two transformer models (ViT, SWIN). Results: While switching from filtering method 1 to method 2 resulted in a slight decline in human performance, it led to a significant improvement for the networks. What could explain this improvement? One possibility is that networks were simply confused by the smooth shapes produced by method 1. To assess this possibility, we retested the networks using a third filtering method in which phases were randomized and amplitudes set to normative, uninformative values. While performance improved for these more realistic shape stimuli, for the two transformer models (ViT and SWIN), performance remained below levels seen with method 2, indicating that these networks, unlike humans, are able to make effective use of the amplitude structure of low shape frequency components, even when phases are randomized. Conclusions: While humans use low-frequency shape information more effectively than DNNs, they depend critically on the phase structure of these low-frequency shape components. In contrast, transformer networks exploit the texture-like amplitude structure of these components even when phase is randomized.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".