Department of Psychology, College of the Holy Cross, 1 College Street, Worcester, MA, 01610, USA.
Department of Radiology, University of Massachusetts, Chan Medical School, Worcester, MA, USA.
Cogn Res Princ Implic. 2023 Feb 1;8(1):10. doi: 10.1186/s41235-023-00462-5.
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.
医学专家仅需短暂的半秒呈现,就能根据初始全局图像处理(称为“概貌”)产生的第一印象,判断出她所看到的医学扫描是否异常。概貌处理的性质存在争议,但这种争议源于具有多年感知经验的医学专家的研究结果。本研究旨在确定接受简短感知训练的新手(未经医学培训)参与者是否能够进行医学图像的概貌处理,并深入了解概貌信号的本质。我们对 20 名新手进行了组织学图像的短暂感知适应性训练。训练后,新手观察者能够从组织学图像的短暂 500 毫秒掩蔽呈现中获得异常检测和异常分类的机会,从而显示出“概貌”。在受过感知训练的新手参与者中表现出的全局信号具有多个可分离的成分,其中一些成分与新手参与者在感知学习过程中学习正常模板的速度有关。我们认为,当专家查看他们在培训和职业生涯中接触到的成千上万张图像衍生的医学图像时,可能会出现多个概貌信号。我们还建议,对正常模板进行定向学习可能会提高放射科医生和病理学家的异常检测和识别能力。