Technical Program: Paper Detail || CCN 2023 || 2023 Conference on Cognitive Computational Neuroscience || Oxford, UK || August 24 - 27, 2023

P-2B.116

Self-supervised transformers predict dynamics of object-based attention in humans

Hossein Adeli, Seoyoung Ahn, Stony Brook University, United States; Nikolaus Kriegeskorte, Columbia University, United States; Gregory Zelinsky, Stony Brook University, United States

Session:

Posters 2B Poster

Location:

Marquee

Presentation Time:

Fri, 25 Aug, 13:00 - 15:00 United Kingdom Time

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.

Manuscript:

View Manuscript

License:

Creative Commons License

This work is licensed under a Creative Commons Attribution 3.0 Unported License.

DOI:

10.32470/CCN.2023.1703-0

Publication:

2023 Conference on Cognitive Computational Neuroscience

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Last updated Last updated 22 August 2023.