  Training individuals to make accurate decisions from medical images is a
critical component of education in diagnostic pathology. We describe a joint
experimental and computational modeling approach to examine the similarities
and differences in the cognitive processes of novice participants and
experienced participants (pathology residents and pathology faculty) in cancer
cell image identification. For this study we collected a bank of hundreds of
digital images that were identified by cell type and classified by difficulty
by a panel of expert hematopathologists. The key manipulations in our study
included examining the speed-accuracy tradeoff as well as the impact of prior
expectations on decisions. In addition, our study examined individual
differences in decision-making by comparing task performance to domain general
visual ability (as measured using the Novel Object Memory Test (NOMT) (Richler
et al., 2017). Using Signal Detection Theory (SDT) and the Diffusion Decision
Model (DDM), we found many similarities between expert and novices in our task.
While experts tended to have better discriminability, the two groups responded
similarly to time pressure (i.e., reduced caution under speed instructions in
the DDM) and to the introduction of a probabilistic cue (i.e., increased
response bias in the DDM). These results have important implications for
training in this area as well as using novice participants in research on
medical image perception and decision-making.
