Perceptual learning in the identification of lung cancer in chest radiographs

Li Z. Sha, Yi Ni Toh, Roger W. Remington, Yuhong V. Jiang

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Extensive research has shown that practice yields highly specific perceptual learning of simple visual properties such as orientation and contrast. Does this same learning characterize more complex perceptual skills? Here we investigated perceptual learning of complex medical images. Novices underwent training over four sessions to discriminate which of two chest radiographs contained a tumor and to indicate the location of the tumor. In training, one group received six repetitions of 30 normal/abnormal images, the other three repetitions of 60 normal/abnormal images. Groups were then tested on trained and novel images. To assess the nature of perceptual learning, test items were presented in three formats – the full image, the cutout of the tumor, or the background only. Performance improved across training sessions, and notably, the improvement transferred to the classification of novel images. Training with more repetitions on fewer images yielded comparable transfer to training with fewer repetitions on more images. Little transfer to novel images occurred when tested with just the cutout of the cancer region or just the background, but a larger cutout that included both the cancer region and some surrounding regions yielded good transfer. Perceptual learning contributes to the acquisition of expertise in cancer image perception.

Original languageEnglish (US)
Article number4
JournalCognitive Research: Principles and Implications
Volume5
Issue number1
DOIs
StatePublished - Dec 1 2020

Bibliographical note

Funding Information:
This study was supported by a dissertation research fellowship from the American Psychological Association to Sha Li, and by a seed grant from OFAA-Social Sciences at the University of Minnesota.

PubMed: MeSH publication types

  • Journal Article
  • Research Support, Non-U.S. Gov't

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