ArticlePLoS computational biology2021
An image-computable model of human visual shape similarity.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Human gloss perception reproduced by tiny neural networks.Nature human behaviour · 2026Article
- Human shape perception spontaneously discovers the biological origin of novel, but natural, stimuli.Journal of the Royal Society, Interface · 2025Article
- The Scope and Limits of Fine-Grained Image and Category Information in the Ventral Visual Pathway.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Using drawings and deep neural networks to characterize the building blocks of human visual similarity.Memory & cognition · 2025Article
- Inferring shape transformations in a drawing task.Memory & cognition · 2025Article
- Local organization of spatial and shape information in the primate prefrontal cortex.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Distributed representations of behavior-derived object dimensions in the human visual system.bioRxiv : the preprint server for biology · 2024Article
- High-level aftereffects reveal the role of statistical features in visual shape encoding.Current biology : CB · 2024Article
- DarwinGSE: Towards better image retrieval systems for intellectual property datasets.PloS one · 2024Article
- How well do rudimentary plasticity rules predict adult visual object learning?PLoS computational biology · 2023Article
- Category Trumps Shape as an Organizational Principle of Object Space in the Human Occipitotemporal Cortex.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- Natural scene sampling reveals reliable coarse-scale orientation tuning in human V1.Nature communications · 2022Article
- A Novel Trademark Image Retrieval System Based on Multi-Feature Extraction and Deep Networks.Journal of imaging · 2022Article
- Article
- Contact lenses, the reverse Pulfrich effect, and anti-Pulfrich monovision corrections.Scientific reports · 2020Article
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Authors and funding
7 authors.
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Abstract
Shape is a defining feature of objects, and human observers can effortlessly compare shapes to determine how similar they are. Yet, to date, no image-computable model can predict how visually similar or different shapes appear. Such a model would be an invaluable tool for neuroscientists and could provide insights into computations underlying human shape perception. To address this need, we developed a model ('ShapeComp'), based on over 100 shape features (e.g., area, compactness, Fourier descriptors). When trained to capture the variance in a database of >25,000 animal silhouettes, ShapeComp accurately predicts human shape similarity judgments between pairs of shapes without fitting any parameters to human data. To test the model, we created carefully selected arrays of complex novel shapes using a Generative Adversarial Network trained on the animal silhouettes, which we presented to observers in a wide range of tasks. Our findings show that incorporating multiple ShapeComp dimensions facilitates the prediction of human shape similarity across a small number of shapes, and also captures much of the variance in the multiple arrangements of many shapes. ShapeComp outperforms both conventional pixel-based metrics and state-of-the-art convolutional neural networks, and can also be used to generate perceptually uniform stimulus sets, making it a powerful tool for investigating shape and object representations in the human brain.
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