ArticleNature communications2021
Qualitative similarities and differences in visual object representations between brains and deep networks.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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Who cites it
41 citing papers in PubMed.
- Systematic image perturbations reveal persistent gaps between human and machine vision.iScience · 2026Article
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- How AI can advance psychological science.British journal of psychology (London, England : 1953) · 2026Article
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- Relative size does matter: Animals see optical illusions.Journal of biosciences · 2026Article
- Variation in the geometry of concept manifolds across human visual cortex.PLoS computational biology · 2025Article
- A feedforward mechanism for human-like contour integration.PLoS computational biology · 2025Article
- Text-related functionality and dynamics of visual human pre-frontal activations revealed through neural network convergence.Communications biology · 2025Article
- Concurrent emergence of view invariance, sensitivity to critical features, and identity face classification through visual experience: Insights from deep learning algorithms.Journal of vision · 2025Article
- Beyond human perception: challenges in AI interpretability of orangutan artwork.Primates; journal of primatology · 2025Article
- Establishment of a deep-learning-assisted recurrent nasopharyngeal carcinoma detecting simultaneous tactic (DARNDEST) with high cost-effectiveness based on magnetic resonance images: a multicenter study in an endemic area.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Human-like face pareidolia emerges in deep neural networks optimized for face and object recognition.PLoS computational biology · 2025Article
- A computational deep learning investigation of animacy perception in the human brain.Communications biology · 2024Article
- Artificial intelligence tools trained on human-labeled data reflect human biases: a case study in a large clinical consecutive knee osteoarthritis cohort.Scientific reports · 2024Article
- Using deep neural networks to disentangle visual and semantic information in human perception and memory.Nature human behaviour · 2024Article
- Influence of training and expertise on deep neural network attention and human attention during a medical image classification task.Journal of vision · 2024Article
- Emergence of Emotion Selectivity in Deep Neural Networks Trained to Recognize Visual Objects.bioRxiv : the preprint server for biology · 2024Article
- Integrative processing in artificial and biological vision predicts the perceived beauty of natural images.Science advances · 2024Article
- Emergence of Emotion Selectivity in Deep Neural Networks Trained to Recognize Visual Objects.PLoS computational biology · 2024Article
- Understanding of facial features in face perception: insights from deep convolutional neural networks.Frontiers in computational neuroscience · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Deep neural networks have revolutionized computer vision, and their object representations across layers match coarsely with visual cortical areas in the brain. However, whether these representations exhibit qualitative patterns seen in human perception or brain representations remains unresolved. Here, we recast well-known perceptual and neural phenomena in terms of distance comparisons, and ask whether they are present in feedforward deep neural networks trained for object recognition. Some phenomena were present in randomly initialized networks, such as the global advantage effect, sparseness, and relative size. Many others were present after object recognition training, such as the Thatcher effect, mirror confusion, Weber's law, relative size, multiple object normalization and correlated sparseness. Yet other phenomena were absent in trained networks, such as 3D shape processing, surface invariance, occlusion, natural parts and the global advantage. These findings indicate sufficient conditions for the emergence of these phenomena in brains and deep networks, and offer clues to the properties that could be incorporated to improve deep networks.
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