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Unsupervised learning predicts human perception and misperception of gloss.

Katherine R Storrs1, Barton L Anderson2, Roland W Fleming3,4

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The brain learns to perceive surface glossiness by modeling statistical image structures. Unsupervised neural networks trained on images accurately predict human gloss perception and illusions.

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Area of Science:

  • Computer Vision
  • Computational Neuroscience
  • Perception

Background:

  • Image perception involves disentangling reflectance, lighting, and geometry.
  • Understanding how the brain achieves this, particularly for surface properties like glossiness, remains a challenge.

Purpose of the Study:

  • To investigate if unsupervised learning of statistical image structures underlies human perception of glossiness.
  • To compare the internal representations of unsupervised neural networks with human gloss judgments.

Main Methods:

  • Trained unsupervised generative neural networks on renderings of glossy surfaces.
  • Compared network representations with human gloss perception data, including successes and errors.
  • Used linear decoding to assess the predictive power of network representations for specular reflectance.

Main Results:

  • Networks spontaneously clustered images by distal properties (reflectance, illumination) without explicit labels.
  • Network representations predicted human gloss perception accuracy and specific perceptual illusions.
  • Decoding specular reflectance from network internal codes outperformed supervised and control models.

Conclusions:

  • Unsupervised learning of statistical image structure is a plausible mechanism for perceiving surface properties like glossiness.
  • This approach may explain various perceptual dimensions in vision and other sensory domains.