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Perceptual learning in humans: An active, top-down-guided process.

Heleen A Slagter1

  • 1Department of Cognitive Psychology, Institute for Brain and Behavior Amsterdam, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands h.a.slagter@vu.nlhttps://research.vu.nl/en/persons/heleen-slagter.

The Behavioral and Brain Sciences
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Summary
This summary is machine-generated.

Developing deep neural networks (DNNs) for human-like vision requires moving beyond blank slate training. Instead, DNNs should incorporate top-down guidance, mirroring human perceptual learning for more accurate models.

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Area of Science:

  • Cognitive Science
  • Computer Vision
  • Artificial Intelligence

Background:

  • Current deep neural network (DNN) models for human-like vision often use a 'blank slate' approach with visual image training data.
  • This method contrasts with established theories of human perception and perceptual learning.

Purpose of the Study:

  • To propose a shift in DNN development for human vision modeling.
  • To integrate principles of active, top-down-guided perception into artificial vision systems.

Main Methods:

  • Reviewing literature on human perception and perceptual learning.
  • Conceptualizing a revised approach for training DNNs based on active perception principles.

Main Results:

  • Standard DNN training may not fully capture human-like visual processing.
  • An active, top-down-guided approach is suggested as crucial for developing more human-like DNN vision models.

Conclusions:

  • Developing DNNs that accurately model human vision necessitates a departure from passive, bottom-up processing.
  • Future DNN architectures should embrace active, top-down-guided mechanisms informed by human perceptual learning.