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
Citations
Student Authors
Faculty Advisor
Academic Program
UMass Chan Affiliations
Document Type
Publication Date
Subject Area
Collections
Embargo Expiration Date
Link to Full Text
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.
Source
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.