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Updated: May 12, 2026
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Creating Objects and Object Categories for Studying Perception and Perceptual Learning
Published on: November 2, 2012
Profiles of visual perceptual learning in feature space
Shiqi Shen1,2, Yueling Sun1,2, Jiachen Lu1,2
1Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, South China Normal University, Guangzhou, Guangdong 510631, China.
Iscience
|February 22, 2024
Summary
Visual perceptual learning (VPL) shows different feature profiles depending on task complexity. Low-level VPL has a center-surround profile, while high-level VPL exhibits a monotonic gradient profile.
Area of Science:
- Cognitive Neuroscience
- Computational Neuroscience
- Visual Perception
Background:
- Visual perceptual learning (VPL) describes experience-induced improvements in visual discrimination.
- The precise profile of VPL within feature space remains incompletely understood.
- Understanding VPL's feature space profile is crucial for elucidating neural computation mechanisms.
Purpose of the Study:
- To investigate and characterize the feature space profile of VPL for both low-level and high-level visual discrimination tasks.
- To determine if a deep convolutional neural network can replicate observed VPL profiles.
- To establish a feature hierarchy-dependent understanding of VPL.
Main Methods:
- Human subjects trained on grating orientation (low-level) and face view (high-level) discrimination tasks over an extended period.
- Assessment of VPL profiles during, immediately after, and one month post-training.
- Utilized a modified AlexNet deep convolutional neural network (7 and 12 layers) to model VPL profiles.
Main Results:
- VPL for grating orientation discrimination demonstrated a center-surround profile in feature space.
- VPL for face view discrimination exhibited a monotonic gradient profile in feature space.
- A deep convolutional neural network successfully replicated both observed VPL profiles.
Conclusions:
- This study reveals, for the first time, a feature hierarchy-dependent profile of VPL.
- The distinct profiles (center-surround vs. monotonic gradient) are linked to the complexity of the visual features being learned.
- These findings provide critical constraints for understanding the neural computations underlying VPL.
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