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Perceptual Expertise and Attention: An Exploration using Deep Neural Networks
Soukhin Das1,2, G R Mangun1,2,3, Mingzhou Ding3
1Center for Mind and Brain, University of California, Davis.
Biorxiv : the Preprint Server for Biology
|October 28, 2024
Summary
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.
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.
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