ArticleProceedings of the National Academy of Sciences of the United States of America2022
Texture-like representation of objects in human visual cortex.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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39 citing papers in PubMed.
- Systematic image perturbations reveal persistent gaps between human and machine vision.iScience · 2026Article
- MAME: Multidimensional adaptive metamer exploration with human perceptual feedback.Journal of vision · 2026Article
- The reading brain - Transforming vision into language.Progress in neurobiology · 2026Review
- Neuronal tuning aligns dynamically with object and texture manifolds across the visual hierarchy.Nature neuroscience · 2026Article
- Neuroradiological Insights into Visual Mental Imagery: Structural and Functional Imaging of Ventral and Dorsal Streams.Brain sciences · 2026Review
- Achieving more human brain-like vision via human EEG representational alignment.Communications biology · 2026Article
- From adolescence to adulthood: functional fingerprints of high-level visual cortex reveal differential development of visuospatial processing.bioRxiv : the preprint server for biology · 2026Article
- Natural Scene and Object Perception Based on Statistical Image Features: Psychophysics and EEG.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Three-dimensional shape cues affect human and artificial recognition systems differently.PloS one · 2026Article
- Investigating action topography in visual cortex and deep artificial neural networks.Nature communications · 2025Article
- Mapping macaque to human cortex with natural scene responses.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Scaling of Haralick features with image bit depth and gray level co-occurrence matrix displacement vector for linear gradients.Computers in biology and medicine · 2025Article
- Fast and robust visual object recognition in young children.Science advances · 2025Article
- Responses of neurons in macaque V4 to object and texture images.bioRxiv : the preprint server for biology · 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
- Approximating Human-Level 3D Visual Inferences With Deep Neural Networks.Open mind : discoveries in cognitive science · 2025Article
- Scaling laws for Haralick texture features of linear gradients.PeerJ. Computer science · 2025Article
- Dimensions underlying the representational alignment of deep neural networks with humans.Nature machine intelligence · 2025Article
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2 authors.
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Abstract
The human visual ability to recognize objects and scenes is widely thought to rely on representations in category-selective regions of the visual cortex. These representations could support object vision by specifically representing objects, or, more simply, by representing complex visual features regardless of the particular spatial arrangement needed to constitute real-world objects, that is, by representing visual textures. To discriminate between these hypotheses, we leveraged an image synthesis approach that, unlike previous methods, provides independent control over the complexity and spatial arrangement of visual features. We found that human observers could easily detect a natural object among synthetic images with similar complex features that were spatially scrambled. However, observer models built from BOLD responses from category-selective regions, as well as a model of macaque inferotemporal cortex and Imagenet-trained deep convolutional neural networks, were all unable to identify the real object. This inability was not due to a lack of signal to noise, as all observer models could predict human performance in image categorization tasks. How then might these texture-like representations in category-selective regions support object perception? An image-specific readout from category-selective cortex yielded a representation that was more selective for natural feature arrangement, showing that the information necessary for natural object discrimination is available. Thus, our results suggest that the role of the human category-selective visual cortex is not to explicitly encode objects but rather to provide a basis set of texture-like features that can be infinitely reconfigured to flexibly learn and identify new object categories.
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