Lightness Illusions Through AI Eyes: Assessing ConvNet and ViT Concordance with Human Perception
Bibliographic record
Abstract
Inferring surface reflectance from luminance images has proven to be a challenge for models of human vision, as many combinations of illumination, reflectance, and 3D shape can create the same luminance image. Traditional models struggle with this deep ambiguity. Recently, convolutional neural networks (CNNs) and vision transformers (ViTs) have been successful computer vision approaches to inferring surface colour. These architectures have the potential to be foundational models for lightness and color perception, if they process image information similarly to humans. We trained CNN and ViT backbones including ResNet18, VGG19, DPT, and custom designs to infer surface reflectance from luminance images using a custom dataset of luminance and reflectance images generated in Blender. We used these models to infer surface reflectance from several well-known images that generate strong lightness illusions, including the argyle, Koffka-Adelson, snake, simultaneous contrast, White's, and checkerboard assimilation illusions, as well as their control images. These illusions are often thought to result from the visual system's attempt to infer surface reflectance from ambiguous images using the statistics of natural images, and we hypothesized that networks trained on simple scenes rendered with shading and shadows would be susceptible to similar illusions. We found that all networks did in fact predict illusions in most test images, and predicted stronger illusions than in the control conditions. The exceptions were that the models typically failed to predict the argyle illusion, and to predict assimilation illusions. Model saliency analysis showed that the networks' outputs were strongly dependent on pixel-information in the shadowed regions of the image. These results support the hypothesis that some lightness phenomena arise from the visual system's use of natural scene statistics to infer reflectance from ambiguous images, and show the potential of CNNs and other deep learning architectures as starting points for models of human lightness and colour perception.
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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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".