# Human peripheral blur is optimal for object recognition

arXiv: https://arxiv.org/abs/1807.08476 · HTML: https://mlsanity.com/paper/1807.08476/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2018.

## Built on by (most cited first, 8 of 8)

- [PeLK: Parameter-efficient Large Kernel ConvNets with Peripheral Convolution](https://mlsanity.com/paper/2403.07589/) (2024)
- [On the use of Cortical Magnification and Saccades as Biological Proxies for Data Augmentation](https://mlsanity.com/paper/2112.07173/) (2021)
- [Neuromorphic Computing and Sensing in Space](https://mlsanity.com/paper/2212.05236/) (2022)
- [LLMind: Bio-inspired Training-free Adaptive Visual Representations for Vision-Language Models](https://mlsanity.com/paper/2603.14882/) (2026)
- [Training on Foveated Images Improves Robustness to Adversarial Attacks](https://mlsanity.com/paper/2308.00854/) (2023)
- [Human Eyes Inspired Recurrent Neural Networks are More Robust Against Adversarial Noises](https://mlsanity.com/paper/2206.07282/) (2022)
- [What if Eye...? Computationally Recreating Vision Evolution](https://mlsanity.com/paper/2501.15001/) (2025)
- [Brain-like emergent properties in deep networks: impact of network architecture, datasets and training](https://mlsanity.com/paper/2411.16326/) (2024)

## Abstract (arXiv)

Our vision is sharpest at the center of our gaze and becomes progressively blurry into the periphery. It is widely believed that this high foveal resolution evolved at the expense of peripheral acuity. But what if this sampling scheme is actually optimal for object recognition? To test this hypothesis, we trained deep neural networks on 'foveated' images with high resolution near objects and increasingly sparse sampling into the periphery. Neural networks trained using a blur profile matching the human eye yielded the best performance compared to shallower and steeper blur profiles. Even in humans, categorization accuracy deteriorated only for steeper blur profiles. Thus, our blurry peripheral vision may have evolved to optimize object recognition rather than merely due to wiring constraints.