ArticleScientific reports2020
Orthogonal Representations of Object Shape and Category in Deep Convolutional Neural Networks and Human Visual Cortex.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Investigating action topography in visual cortex and deep artificial neural networks.Nature communications · 2025Article
- Adaptive stretching of representations across brain regions and deep learning model layers.Nature communications · 2025Article
- Protein Language Models Expose Viral Immune Mimicry.Viruses · 2025Article
- Coordinating multiple mental faculties during learning.Scientific reports · 2025Article
- A computational deep learning investigation of animacy perception in the human brain.Communications biology · 2024Article
- Graspable foods and tools elicit similar responses in visual cortex.Cerebral cortex (New York, N.Y. : 1991) · 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
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- Graspable foods and tools elicit similar responses in visual cortex.bioRxiv : the preprint server for biology · 2024Article
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- Neural similarity between overlapping events at learning differentially affects reinstatement across the cortex.NeuroImage · 2023Article
- Spikiness and animacy as potential organizing principles of human ventral visual cortex.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Equivalent processing of facial expression and identity by macaque visual system and task-optimized neural network.NeuroImage · 2023Article
- The promise and peril of interactive embodied agents for studying non-verbal communication: a machine learning perspective.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2023Review
- 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
- The representational hierarchy in human and artificial visual systems in the presence of object-scene regularities.PLoS computational biology · 2023Article
- Deep Neural Networks and Visuo-Semantic Models Explain Complementary Components of Human Ventral-Stream Representational Dynamics.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- A deep neural network model of the primate superior colliculus for emotion recognition.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2022Article
- Texture-like representation of objects in human visual cortex.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
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4 authors.
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Abstract
Deep Convolutional Neural Networks (CNNs) are gaining traction as the benchmark model of visual object recognition, with performance now surpassing humans. While CNNs can accurately assign one image to potentially thousands of categories, network performance could be the result of layers that are tuned to represent the visual shape of objects, rather than object category, since both are often confounded in natural images. Using two stimulus sets that explicitly dissociate shape from category, we correlate these two types of information with each layer of multiple CNNs. We also compare CNN output with fMRI activation along the human visual ventral stream by correlating artificial with neural representations. We find that CNNs encode category information independently from shape, peaking at the final fully connected layer in all tested CNN architectures. Comparing CNNs with fMRI brain data, early visual cortex (V1) and early layers of CNNs encode shape information. Anterior ventral temporal cortex encodes category information, which correlates best with the final layer of CNNs. The interaction between shape and category that is found along the human visual ventral pathway is echoed in multiple deep networks. Our results suggest CNNs represent category information independently from shape, much like the human visual system.
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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.