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Perceptual Expertise and Attention: An Exploration using Deep Neural Networks.

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Perceptual expertise enhances object recognition, with feature-based attention (FBA) boosting performance only within the expert domain. Attention outside expertise showed reduced benefits, highlighting the role of neural tuning.

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Area of Science:

  • Cognitive Neuroscience
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Perceptual expertise and attention are crucial for object recognition and task performance.
  • Attention mechanisms are thought to differ between experts and novices, but this remains under-investigated.

Purpose of the Study:

  • To investigate the interplay between perceptual expertise and attention.
  • To explore how feature-based attention (FBA) influences recognition within and outside an expert domain using computational models.

Main Methods:

  • Utilized convolutional neural networks (CNNs) as models of primate visual pathways.
  • Trained two CNN models to specialize in either face or scene recognition.
  • Evaluated the impact of FBA on performance with complex stimuli, including superimposed images.

Main Results:

  • Expert models showed superior performance in their trained domain.
  • FBA significantly enhanced performance (up to 35% for scenes, 15% for faces) but only within the domain of expertise.
  • Attention outside the expertise domain yielded reduced or negative effects on performance.

Conclusions:

  • Expertise leads to enhanced neural tuning for category-specific features, improving recognition by reducing representational competition.
  • CNNs serve as valuable computational tools for neuroscience research, particularly for studying attention and expertise.
  • Neural tuning is critical for differentiating attention effects in experts versus novices.