ArticleFrontiers in psychology2016
Image Statistics and the Representation of Material Properties in the Visual Cortex.
Article in Frontiers in psychology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Development of detection sensitivity to material properties in school-age children.Scientific reports · 2026Article
- Probing the link between vision and language in material perception using psychophysics and unsupervised learning.PLoS computational biology · 2024Article
- Probing the Link Between Vision and Language in Material Perception Using Psychophysics and Unsupervised Learning.bioRxiv : the preprint server for biology · 2024Article
- The role of texture summary statistics in material recognition from drawings and photographs.Journal of vision · 2023Article
- A Texture Statistics Encoding Model Reveals Hierarchical Feature Selectivity across Human Visual Cortex.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- Pulvinar Response Profiles and Connectivity Patterns to Object Domains.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- Sustainable materials: a linking bridge between material perception, affordance, and aesthetics.Frontiers in psychology · 2023Review
- Visual segmentation of complex naturalistic structures in an infant eye-tracking search task.PloS one · 2022Article
- How usefulness shapes neural representations during goal-directed behavior.Science advances · 2021Article
- Painterly depiction of material properties.Journal of vision · 2020Article
- Children's use of local and global visual features for material perception.Journal of vision · 2020Article
- Children's use of visual summary statistics for material categorization.Journal of vision · 2017Article
Corrections and comments
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Authors and funding
2 authors.
Funding
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Abstract
We explored perceived material properties (roughness, texturedness, and hardness) with a novel approach that compares perception, image statistics and brain activation, as measured with fMRI. We initially asked participants to rate 84 material images with respect to the above mentioned properties, and then scanned 15 of the participants with fMRI while they viewed the material images. The images were analyzed with a set of image statistics capturing their spatial frequency and texture properties. Linear classifiers were then applied to the image statistics as well as the voxel patterns of visually responsive voxels and early visual areas to discriminate between images with high and low perceptual ratings. Roughness and texturedness could be classified above chance level based on image statistics. Roughness and texturedness could also be classified based on the brain activation patterns in visual cortex, whereas hardness could not. Importantly, the agreement in classification based on image statistics and brain activation was also above chance level. Our results show that information about visual material properties is to a large degree contained in low-level image statistics, and that these image statistics are also partially reflected in brain activity patterns induced by the perception of material images.
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