Title |
Multiple expressions of “expert” abnormality gist in novices following perceptual learning
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Published in |
Cognitive Research: Principles and Implications, February 2023
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DOI | 10.1186/s41235-023-00462-5 |
Pubmed ID | |
Authors |
Gregory J. DiGirolamo, Megan DiDominica, Muhammad A. J. Qadri, Philip J. Kellman, Sally Krasne, Christine Massey, Max P. Rosen |
Abstract |
With a brief half-second presentation, a medical expert can determine at above chance levels whether a medical scan she sees is abnormal based on a first impression arising from an initial global image process, termed "gist." The nature of gist processing is debated but this debate stems from results in medical experts who have years of perceptual experience. The aim of the present study was to determine if gist processing for medical images occurs in naïve (non-medically trained) participants who received a brief perceptual training and to tease apart the nature of that gist signal. We trained 20 naïve participants on a brief perceptual-adaptive training of histology images. After training, naïve observers were able to obtain abnormality detection and abnormality categorization above chance, from a brief 500 ms masked presentation of a histology image, hence showing "gist." The global signal demonstrated in perceptually trained naïve participants demonstrated multiple dissociable components, with some of these components relating to how rapidly naïve participants learned a normal template during perceptual learning. We suggest that multiple gist signals are present when experts view medical images derived from the tens of thousands of images that they are exposed to throughout their training and careers. We also suggest that a directed learning of a normal template may produce better abnormality detection and identification in radiologists and pathologists. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 9 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Bachelor | 2 | 22% |
Unspecified | 1 | 11% |
Student > Ph. D. Student | 1 | 11% |
Other | 1 | 11% |
Unknown | 4 | 44% |
Readers by discipline | Count | As % |
---|---|---|
Psychology | 2 | 22% |
Unspecified | 1 | 11% |
Computer Science | 1 | 11% |
Business, Management and Accounting | 1 | 11% |
Unknown | 4 | 44% |