# Emergence of Human-Like Attention in Self-Supervised Vision Transformers: an eye-tracking study

arXiv: https://arxiv.org/abs/2410.22768 · HTML: https://mlsanity.com/paper/2410.22768/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2024.

## Built on by (most cited first, 4 of 4)

- [Learning to See Through a Baby's Eyes: Early Visual Diets Enable Robust Visual Intelligence in Humans and Machines](https://mlsanity.com/paper/2511.14440/) (2025)
- [Vision Transformer attention alignment with human visual perception in aesthetic object evaluation](https://mlsanity.com/paper/2507.17616/) (2025)
- [Attention Alignment Between Humans and Vision-Language Models](https://mlsanity.com/paper/2606.17410/) (2026)
- [A recurrent vision transformer shows signatures of primate visual attention](https://mlsanity.com/paper/2502.10955/) (2025)

## Abstract (arXiv)

Many models of visual attention have been proposed so far. Traditional bottom-up models, like saliency models, fail to replicate human gaze patterns, and deep gaze prediction models lack biological plausibility due to their reliance on supervised learning. Vision Transformers (ViTs), with their self-attention mechanisms, offer a new approach but often produce dispersed attention patterns if trained with supervised learning. This study explores whether self-supervised DINO (self-DIstillation with NO labels) training enables ViTs to develop attention mechanisms resembling human visual attention. Using video stimuli to capture human gaze dynamics, we found that DINO-trained ViTs closely mimic human attention patterns, while those trained with supervised learning deviate significantly. An analysis of self-attention heads revealed three distinct clusters: one focusing on foreground objects, one on entire objects, and one on the background. DINO-trained ViTs offer insight into how human overt attention and figure-ground separation develop in visual perception.