Representational Smoothing to Improve Medical Image Decision Making

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

Harnessing the Wisdom of the Confident Crowd in Medical Image Decision-making

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The Role of Salience in Multialternative Multiattribute Choice

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

The Role of Salience in Multialternative Multiattribute Choice

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