ArticleScientific reports2017
A data driven approach to understanding the organization of high-level visual cortex.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Multidimensional Feature Tuning in Category Selective Areas of Human Visual Cortex.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Spikiness and animacy as potential organizing principles of human ventral visual cortex.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Spatial Scene Memories Are Biased Towards a Fixed Amount of Semantic Information.Open mind : discoveries in cognitive science · 2023Article
- The representation of shape and texture in category-selective regions of ventral-temporal cortex.The European journal of neuroscience · 2022Article
- Disentangling the Independent Contributions of Visual and Conceptual Features to the Spatiotemporal Dynamics of Scene Categorization.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2020Article
- What do across-subject analyses really tell us about neural coding?Neuropsychologia · 2020Article
- A data-driven approach to stimulus selection reveals an image-based representation of objects in high-level visual areas.Human brain mapping · 2019Article
- Similarity judgments and cortical visual responses reflect different properties of object and scene categories in naturalistic images.NeuroImage · 2019Article
- Spatial frequency supports the emergence of categorical representations in visual cortex during natural scene perception.NeuroImage · 2018Article
- The Organization and Operation of Inferior Temporal Cortex.Annual review of vision science · 2018Review
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Authors and funding
3 authors.
Funding
Abstract
The neural representation in scene-selective regions of human visual cortex, such as the PPA, has been linked to the semantic and categorical properties of the images. However, the extent to which patterns of neural response in these regions reflect more fundamental organizing principles is not yet clear. Existing studies generally employ stimulus conditions chosen by the experimenter, potentially obscuring the contribution of more basic stimulus dimensions. To address this issue, we used a data-driven approach to describe a large database of scenes (>100,000 images) in terms of their visual properties (orientation, spatial frequency, spatial location). K-means clustering was then used to select images from distinct regions of this feature space. Images in each cluster did not correspond to typical scene categories. Nevertheless, they elicited distinct patterns of neural response in the PPA. Moreover, the similarity of the neural response to different clusters in the PPA could be predicted by the similarity in their image properties. Interestingly, the neural response in the PPA was also predicted by perceptual responses to the scenes, but not by their semantic properties. These findings provide an image-based explanation for the emergence of higher-level representations in scene-selective regions of the human brain.
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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.