We demonstrate how medical-image classification decisions can be denoised by aggregating decisions on similar images. In our algorithm, the final decision on a target image is cancerous if a percentage t of the k most similar images are cancerous, else it is not cancerous. Similarity between images is calculated as the distance between representations from an artificial neural network. We vary k and t for novice and expert participants using data from Trueblood et al. (2018) and Trueblood et al. (2021). We show that increasing k improves performance for novices, with their performance approaching that of experts. We also show that the algorithm is biased towards identifying cancerous cells, which is reflected in the representational space. The percentage t allows greater control over sensitivity and specificity and can be used to debias decisions. This algorithm is less effective for experts, partially explained by them giving similar responses on similar images.

Attention plays a central role in multi-alternative multiat- tribute decision-making but the cognitive mechanisms for it are elusive (Yang & Krajbich, 2023; Molter, Thomas, Huet- tel, Heekeren, & Mohr, 2022; Trueblood, 2022). In this project, we explored the role of bottom-up attention by manipulating the salience of different options in a multi-alternative, multi-attribute choice display. Behaviorally, we observed that salience interacts with choice, where the salient option is selected more often, especially in quick decisions. Using computational modeling, we tested two different hypotheses for how salience impacts decision-making for different individuals. We tested (i) if salience created an initial bias in the decision-making process, and (ii) if salience impacted the comparisons that are made during the decision-making process. We find that there are large individual differences in the mechanism through which salience impacts choice. For many individuals, there was no impact of salience. However, for a sizable minority, salience created an initial boost in selecting the salient option. We do not find strong evidence for the impact of salience in the comparison process. In exploratory analyses, we observe that the impact of salience in decision-making is correlated with thinking styles. Our results indicate that salience-driven attention might impact decision-making in different ways for individuals.

Improving the accuracy of medical image interpretation is critical to improving the diagnosis of many diseases. Using both novices (undergraduates) and experts (medical professionals), we investigate methods for improving the accuracy of a single decision maker by aggregating repeated decisions from an individual in different ways. Our participants made classification decisions (cancerous versus non-cancerous) and confidence judgments on a series of cell images, viewing and classifying each image twice. We first applied the maximum confidence slating algorithm (Koriat, 2012), which leverages metacognitive ability by using the most confident response for an image as the `final response'. We also examined algorithms that aggregated decisions based on image similarity, leveraging neural network models to determine similarity. We found maximum confidence slating improves classification accuracy for both novices and experts. However, aggregating responses on similar images improves classification accuracy for novices and not experts, suggesting differences in the decision mechanisms of novices and experts.