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Supervised Learning Occurs in Visual Perceptual Learning of Complex Natural Images
Sebastian M Frank1, Andrea Qi1, Daniela Ravasio1
1Brown University, Department of Cognitive, Linguistic, and Psychological Sciences, 190 Thayer Street, Providence, RI 02912, USA.
Current Biology : CB
|June 6, 2020
Summary
Supervised learning drives visual perceptual learning (VPL) for complex images. Detailed feedback is crucial for learning and retaining detection of architectural distortions and microcalcifications in mammograms.
Area of Science:
- Cognitive Science
- Neuroscience
- Medical Imaging
Background:
- Visual perceptual learning (VPL) mechanisms, supervised vs. unsupervised, are debated, often based on simple feedback in low-level feature tasks.
- Previous studies focused on accuracy feedback, limiting understanding of VPL for complex natural images.
Purpose of the Study:
- To investigate the role of feedback content in acquiring and retaining VPL for complex natural images, specifically mammographic lesions.
- To determine if different types of complex lesions require varying feedback for VPL.
Main Methods:
- Trained 72 subjects on detecting mammographic microcalcifications or architectural distortions with no, partial (correctness), or detailed (correctness and location) feedback.
- Assessed VPL acquisition and 6-month retention of lesion detection skills.
Main Results:
- Partial feedback was sufficient for VPL of microcalcifications, while detailed feedback was necessary for architectural distortions.
- Detailed feedback during training was essential for long-term (6-month) retention of VPL for both lesion types.
- VPL requirements vary based on lesion complexity, indicating differential supervision needs.
Conclusions:
- Supervised learning plays a significant role in VPL for complex natural images, but the degree of supervision needed varies.
- Feedback content is critical for VPL acquisition and long-term retention of mammographic lesion detection.
- Findings have implications for optimizing radiologist training in interpreting mammograms.
Keywords:
breast cancerfeedbackhigh-level visionmammogramnatural stimuliradiologysupervised learningunsupervised learningvisual perceptual learningMore Related Videos
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