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Training individuals to make accurate decisions from medical images is acritical component of education in diagnostic pathology. We describe a jointexperimental and computational modeling approach to examine the similaritiesand differences in the cognitive processes of novice participants andexperienced participants (pathology residents and pathology faculty) in cancercell image identification. For this study we collected a bank of hundreds ofdigital images that were identified by cell type and classified by difficultyby a panel of expert hematopathologists. The key manipulations in our studyincluded examining the speed-accuracy tradeoff as well as the impact of priorexpectations on decisions. In addition, our study examined individualdifferences in decision-making by comparing task performance to domain generalvisual ability (as measured using the Novel Object Memory Test (NOMT) (Richleret al., 2017). Using Signal Detection Theory (SDT) and the Diffusion DecisionModel (DDM), we found many similarities between expert and novices in our task.While experts tended to have better discriminability, the two groups respondedsimilarly to time pressure (i.e., reduced caution under speed instructions inthe DDM) and to the introduction of a probabilistic cue (i.e., increasedresponse bias in the DDM). These results have important implications fortraining in this area as well as using novice participants in research onmedical image perception and decision-making.

The Impact of Speed and Bias on the Cognitive Processes of Experts and Novices in Medical Image Decision-making