ArticlePLoS computational biology2016
Deep Neural Networks as a Computational Model for Human Shape Sensitivity.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 100 papers.
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Who cites it
100 citing papers in PubMed, 323 citations in OpenAlex.
- Systematic image perturbations reveal persistent gaps between human and machine vision.iScience · 2026Article
- Can neural networks model the human perception of geometric shapes?PLoS computational biology · 2026Article
- Tracing aesthetic experience from perception and conception to appraisal using deep convolutional neural networks.iScience · 2026Article
- Drawings of THINGS: A large-scale drawing dataset of 1854 object concepts.Behavior research methods · 2026Article
- A Resource-Rational Account of Human Eye Movements During Immersive Visual Search.Open mind : discoveries in cognitive science · 2026Article
- Three-dimensional shape cues affect human and artificial recognition systems differently.PloS one · 2026Article
- Task-optimized models of sensory uncertainty reproduce human confidence judgments.bioRxiv : the preprint server for biology · 2025Article
- Synthesizing images to map neural networks to the human brain.bioRxiv : the preprint server for biology · 2025Article
- A feedforward mechanism for human-like contour integration.PLoS computational biology · 2025Article
- Human shape perception spontaneously discovers the biological origin of novel, but natural, stimuli.Journal of the Royal Society, Interface · 2025Article
- Unraveling the complexity of rat object vision requires a full convolutional network and beyond.Patterns (New York, N.Y.) · 2025Article
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- 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
- Dimensions underlying the representational alignment of deep neural networks with humans.Nature machine intelligence · 2025Article
- Using drawings and deep neural networks to characterize the building blocks of human visual similarity.Memory & cognition · 2025Article
- Dorsoventral comparison of intraspecific variation in the butterfly wing pattern using a convolutional neural network.Biology letters · 2025Article
- A computational deep learning investigation of animacy perception in the human brain.Communications biology · 2024Article
- Fine-grained knowledge about manipulable objects is well-predicted by contrastive language image pre-training.iScience · 2024Article
- Neuronal tuning and population representations of shape and category in human visual cortex.Nature communications · 2024Article
- Using deep neural networks to disentangle visual and semantic information in human perception and memory.Nature human behaviour · 2024Article
40 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Theories of object recognition agree that shape is of primordial importance, but there is no consensus about how shape might be represented, and so far attempts to implement a model of shape perception that would work with realistic stimuli have largely failed. Recent studies suggest that state-of-the-art convolutional 'deep' neural networks (DNNs) capture important aspects of human object perception. We hypothesized that these successes might be partially related to a human-like representation of object shape. Here we demonstrate that sensitivity for shape features, characteristic to human and primate vision, emerges in DNNs when trained for generic object recognition from natural photographs. We show that these models explain human shape judgments for several benchmark behavioral and neural stimulus sets on which earlier models mostly failed. In particular, although never explicitly trained for such stimuli, DNNs develop acute sensitivity to minute variations in shape and to non-accidental properties that have long been implicated to form the basis for object recognition. Even more strikingly, when tested with a challenging stimulus set in which shape and category membership are dissociated, the most complex model architectures capture human shape sensitivity as well as some aspects of the category structure that emerges from human judgments. As a whole, these results indicate that convolutional neural networks not only learn physically correct representations of object categories but also develop perceptually accurate representational spaces of shapes. An even more complete model of human object representations might be in sight by training deep architectures for multiple tasks, which is so characteristic in human development.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.