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<p>Prior work suggests that category learning can reshape perception by shifting perceived similarity among instances. Most evidence comes from artificial stimuli and small category sets. We examined perceptual change in a complex, real-world task: classifying benign and cancerous skin lesions. Medically naive participants rated pairwise similarity among instances from 10 categories before and after comparison-based training with &gt; 500 examples. Training increased perceived similarity for same-category pairs (within-category compression) and decreased it for different-category pairs (between-category expansion). The magnitude of perceptual change predicted generalization to novel instances: greater compression was associated with higher post-training accuracy, and greater expansion with lower false-alarm rates. A label-overlap manipulation showed no differential change, arguing against strategic label retrieval. These findings indicate that perceptual learning systematically reshapes category representations and perceptual experience—promoting within-category compression and between-category expansion—in a challenging, naturally structured domain.</p>

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Categorization-driven perceptual learning changes perceived similarity in the naturalistic domain of skin lesion diagnosis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月31日

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  • Victoria L. Jacoby,
  • Philip J. Kellman

摘要

Prior work suggests that category learning can reshape perception by shifting perceived similarity among instances. Most evidence comes from artificial stimuli and small category sets. We examined perceptual change in a complex, real-world task: classifying benign and cancerous skin lesions. Medically naive participants rated pairwise similarity among instances from 10 categories before and after comparison-based training with > 500 examples. Training increased perceived similarity for same-category pairs (within-category compression) and decreased it for different-category pairs (between-category expansion). The magnitude of perceptual change predicted generalization to novel instances: greater compression was associated with higher post-training accuracy, and greater expansion with lower false-alarm rates. A label-overlap manipulation showed no differential change, arguing against strategic label retrieval. These findings indicate that perceptual learning systematically reshapes category representations and perceptual experience—promoting within-category compression and between-category expansion—in a challenging, naturally structured domain.