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The spreading of attention has been proposed as a mechanism for how humansgroup features to segment objects. However, such a mechanism has not yet beenimplemented and tested in naturalistic images. Here, we leverage the featuremaps from self-supervised vision Transformers and propose a model of humanobject-based attention spreading and segmentation. Attention spreads within anobject through the feature affinity signal between different patches of theimage. We also collected behavioral data on people grouping objects in naturalimages by judging whether two dots are on the same object or on two differentobjects. We found that our models of affinity spread that were built on featuremaps from the self-supervised Transformers showed significant improvement overbaseline and CNN based models on predicting reaction time patterns of humans,despite not being trained on the task or with any other object labels. Our workprovides new benchmarks for evaluating models of visual representation learningincluding Transformers.

Affinity-based Attention in Self-supervised Transformers Predicts Dynamics of Object Grouping in Humans