ArticleThe Journal of neuroscience : the official journal of the Society for Neuroscience2020
Disentangling the Independent Contributions of Visual and Conceptual Features to the Spatiotemporal Dynamics of Scene Categorization.
Article in The Journal of neuroscience : the official journal of the Society for Neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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32 citing papers in PubMed, 71 citations in OpenAlex.
- During natural vision, semantic novelty modulates fixation-related processing in primate cortex.bioRxiv : the preprint server for biology · 2026Article
- Three-dimensional scene boundary representations for wall orientation and distance are represented distinctly in the human visual cortex.PLoS 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
- Article
- Where do I go? Decoding temporal neural dynamics of scene processing and visuospatial memory interactions using convolutional neural networks.Journal of vision · 2025Article
- Human EEG and artificial neural networks reveal disentangled representations and processing timelines of object real-world size and depth in natural images.bioRxiv : the preprint server for biology · 2025Article
- Function over form: The temporal evolution of affordance-based scene categorization.Journal of vision · 2025Article
- The role of anchor objects in scene function understanding.Scientific reports · 2025Article
- Representation of locomotive action affordances in human behavior, brains, and deep neural networks.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Cortical Encoding of Spatial Structure and Semantic Content in 3D Natural Scenes.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- The brain prioritizes the basic level of object category abstraction.Scientific reports · 2025Article
- Neuromorphic computing at scale.Nature · 2025Review
- Distractor anticipation during working memory is associated with theta and beta oscillations across spatial scales.Frontiers in integrative neuroscience · 2025Article
- Identifying and characterizing scene representations relevant for categorization behavior.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Anchor objects drive realism while diagnostic objects drive categorization in GAN generated scenes.Communications psychology · 2024Article
- Visual Recognition Memory of Scenes Is Driven by Categorical, Not Sensory, Visual Representations.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Mapping the dynamics of visual feature coding: Insights into perception and integration.PLoS computational biology · 2024Article
- Empirically Identifying and Computationally Modeling the Brain-Behavior Relationship for Human Scene Categorization.Journal of cognitive neuroscience · 2023Article
- Objects sharpen visual scene representations: evidence from MEG decoding.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Disentangling diagnostic object properties for human scene categorization.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Human scene categorization is characterized by its remarkable speed. While many visual and conceptual features have been linked to this ability, significant correlations exist between feature spaces, impeding our ability to determine their relative contributions to scene categorization. Here, we used a whitening transformation to decorrelate a variety of visual and conceptual features and assess the time course of their unique contributions to scene categorization. Participants (both sexes) viewed 2250 full-color scene images drawn from 30 different scene categories while having their brain activity measured through 256-channel EEG. We examined the variance explained at each electrode and time point of visual event-related potential (vERP) data from nine different whitened encoding models. These ranged from low-level features obtained from filter outputs to high-level conceptual features requiring human annotation. The amount of category information in the vERPs was assessed through multivariate decoding methods. Behavioral similarity measures were obtained in separate crowdsourced experiments. We found that all nine models together contributed 78% of the variance of human scene similarity assessments and were within the noise ceiling of the vERP data. Low-level models explained earlier vERP variability (88 ms after image onset), whereas high-level models explained later variance (169 ms). Critically, only high-level models shared vERP variability with behavior. Together, these results suggest that scene categorization is primarily a high-level process, but reliant on previously extracted low-level features.
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Registered trials
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.