Multiple expressions of "expert" abnormality gist in novices following perceptual learning.
Multiple expressions of "expert" abnormality gist in novices following perceptual learning.
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DOI：
10.1186/s41235-023-00462-5
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发表时间：
2023-02-01
影响因子：
4.1
通讯作者：
Rosen, Max P.
中科院分区：
文献类型：
--
作者：
DiGirolamo, Gregory J.;DiDominica, Megan;Qadri, Muhammad A. J.;Kellman, Philip J.;Krasne, Sally;Massey, Christine;Rosen, Max P.
通过简短的半秒演示，医学专家可以根据最初的全局图像处理产生的第一印象，以高于机率的水平确定她看到的医学扫描是否异常，这被称为“要点”。GIST处理的本质尚有争议，但这一争论源于拥有多年知觉经验的医学专家的结果。本研究的目的是确定在接受过短暂知觉训练的天真(非医学训练)参与者中是否发生了对医学图像的GIST处理，并梳理出这种GIST信号的性质。我们对20名天真的参与者进行了一次简短的组织学图像知觉适应性培训。经过训练，天真的观察者能够从简短的500毫秒的组织学图像的蒙面演示中获得异常检测和异常分类的机会，从而显示出“要点”。在经过知觉训练的天真参与者身上显示的全局信号显示了多个可分离的成分，其中一些成分与天真参与者在知觉学习过程中学习正常模板的速度有关。我们认为，当专家查看从他们在整个培训和职业生涯中接触到的数以万计的图像中获得的医学图像时，存在多种主要信号。我们还建议，对正常模板的定向学习可能会在放射科医生和病理学家中产生更好的异常检测和识别。
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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影响因子：
3.7
作者：
Chen W;HolcDorf D;McCusker MW;Gaillard F;Howe PDL
通讯作者：
Howe PDL
影响因子：
8.2
作者：
Greene MR;Oliva A
通讯作者：
Oliva A
影响因子：
2.6
作者：
Greene MR;Oliva A
通讯作者：
Oliva A
影响因子：
5.5
作者：
Drew, Trafton;Evans, Karla;Wolfe, Jeremy M.
通讯作者：
Wolfe, Jeremy M.
影响因子：
4.8
作者：
Nodine, CF;Kundel, HL;Conant, EF
通讯作者：
Conant, EF