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Human peripheral blur is optimal for object recognition.

R T Pramod1, Harish Katti2, S P Arun2

  • 1Department of Electrical Communication Engineering, Indian Institute of Science, Bangalore 560012, India.

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|July 13, 2022
PubMed
Summary
This summary is machine-generated.

Blurry peripheral vision may optimize object recognition. Deep neural networks trained with human-like peripheral blur performed best, suggesting this trait evolved for better visual processing, not just wiring limits.

Keywords:
Blur perceptionConvolutional neural networksDeep learningDeep neural networksObject recognitionPeripheral vision

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Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Vision Science

Background:

  • Human vision exhibits high foveal resolution, with acuity decreasing towards the periphery.
  • This peripheral blur is often attributed to wiring constraints, implying a trade-off with foveal acuity.
  • The functional advantage of this visual sampling strategy remains debated, particularly for object recognition.

Purpose of the Study:

  • To investigate whether the human visual system's peripheral blur profile is optimized for object recognition.
  • To compare the performance of deep neural networks trained with varying degrees of peripheral blur against human data.
  • To challenge the prevailing hypothesis that peripheral blur is solely a consequence of neural wiring limitations.

Main Methods:

  • Deep neural networks were trained using "foveated" images, where object resolution was high and surroundings progressively blurred, mimicking human vision.
  • Networks were trained with blur profiles matching human peripheral vision, as well as shallower and steeper profiles.
  • Human subjects were tested on object categorization tasks using images with varying blur profiles.

Main Results:

  • Networks trained with the human peripheral blur profile achieved superior object recognition performance compared to those with shallower/steeper blur or full-resolution images.
  • This performance improvement was observed even on state-of-the-art networks already trained to saturation.
  • Human subjects' object categorization accuracy decreased significantly only with steeper blur profiles, consistent with their inherent peripheral blur.

Conclusions:

  • The findings suggest that peripheral visual blur may be an evolved mechanism to enhance object recognition efficiency.
  • This challenges the notion that peripheral blur is merely an artifact of neural wiring constraints.
  • The human visual system's sampling strategy appears optimized for robust object identification in naturalistic viewing conditions.