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Central and peripheral vision for scene recognition: A neurocomputational modeling exploration.

Panqu Wang1, Garrison W Cottrell2

  • 1Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, USApawang@ucsd.eduhttp://acsweb.ucsd.edu/~pawang/homepage_PhD/index.html.

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Peripheral vision aids scene recognition accuracy, while central vision offers efficiency. Neurocomputational models replicate these findings, suggesting peripheral features are key and models naturally prioritize peripheral input.

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Area of Science:

  • Cognitive Neuroscience
  • Computational Vision
  • Human Perception

Background:

  • Central vision is typically more efficient for detailed tasks, but peripheral vision plays a crucial role in overall scene understanding.
  • Previous research indicates peripheral vision contributes significantly to scene recognition accuracy, while central vision is more efficient per unit area.

Purpose of the Study:

  • To model and explain the distinct roles of central and peripheral vision in human scene recognition using neurocomputational approaches.
  • To validate findings from Larson and Loschky (2009) and Thibaut et al. (2014) through advanced deep learning models.
  • To investigate the underlying mechanisms of the peripheral advantage in scene categorization.

Main Methods:

  • Utilized state-of-the-art deep neural networks to replicate human scene recognition performance.
  • Employed a deep mixture-of-experts model (The Deep Model, TDM) processing central and peripheral visual information separately.
  • Analyzed feature representations and pathway weighting within the trained models.

Main Results:

  • Deep neural network models successfully replicated both the peripheral advantage in accuracy and the central vision efficiency.
  • The TDM model demonstrated that the peripheral advantage emerges naturally during training, with the model learning to weight the peripheral pathway more.
  • Visualization revealed distinct feature preferences for scene categories in both central and peripheral pathways.

Conclusions:

  • Neurocomputational modeling supports the dual role of vision in scene recognition: peripheral for broad accuracy, central for efficiency.
  • The inherent usefulness of peripheral features is a key factor driving the peripheral advantage.
  • Deep learning models provide a framework for understanding how visual systems learn to integrate information from different visual fields for effective scene categorization.