ArticleProceedings of the ... Conference on Cognitive Computational Neuroscience2023
Self-supervised transformers predict dynamics of object-based attention in humans.
Article in Proceedings of the ... Conference on Cognitive Computational Neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- How the visual brain can learn to parse images using a multiscale, incremental grouping process.PLoS computational biology · 2026Article
- Are we ready to tackle perceptual segmentation of natural scenes?Vision research · 2026Review
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- Natural scene segmentation dynamics reveal iterative Bayesian inference.bioRxiv : the preprint server for biology · 2026Article
- Generating metamers of human scene understanding.... International Conference on Learning Representations · 2026Article
- Article
- A model of thalamo-cortical interaction for incremental binding in mental contour-tracing.PLoS computational biology · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Spread of attention within objects has been proposed as a mechanism for how humans group features to segment objects. However, such a mechanism has not yet been implemented and tested in naturalistic images. Here, we leverage the feature maps from self-supervised vision transformers and propose a model of human object-based attention spreading and segmentation. The attention spreads within an object through the affinity signal between different patches of the image. We show that this model predicts reaction time patterns of people grouping objects in natural images by judging whether two dots are on the same object or on two different objects.
Identifiers
42757389PMC13585476What OpenQuestion holds
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.