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Multiple expressions of "expert" abnormality gist in novices following perceptual learning

DiGirolamo, Gregory J
DiDominica, Megan
Qadri, Muhammad A J
Kellman, Philip J
Krasne, Sally
Massey, Christine
Rosen, Max P
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UMass Chan Affiliations
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Journal Article
Publication Date
2023-02-01
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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.

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DiGirolamo GJ, DiDominica M, Qadri MAJ, Kellman PJ, Krasne S, Massey C, Rosen MP. Multiple expressions of "expert" abnormality gist in novices following perceptual learning. Cogn Res Princ Implic. 2023 Feb 1;8(1):10. doi: 10.1186/s41235-023-00462-5. PMID: 36723822; PMCID: PMC9892374.

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10.1186/s41235-023-00462-5
PubMed ID
36723822
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© The Author(s) 2023. Open Access: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.Attribution 4.0 International