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

TL;DR

This study investigates how speed and bias influence decision-making processes in medical image analysis, comparing novices and experts, revealing similarities and differences with implications for training and research.

Contribution

It combines experimental and computational modeling to analyze cognitive processes in medical image decision-making across different experience levels.

Findings

Experts have better discriminability in image identification.

Both groups respond similarly to time pressure and probabilistic cues.

Training implications for novices and research applications are discussed.

Abstract

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.