Improved classification of mammograms following idealized training

• Idealizing training examples improve people's ability to classify mammograms.
• Idealized training succeeds by reducing the saliency of ambiguous cases.
• Idealized training is most effective when training and test cases share similarities.

People often make decisions by stochastically retrieving a small set of relevant memories. This limited retrieval implies that human performance can be improved by training on idealized category distributions (Giguère & Love, 2013). Here, we evaluate whether the benefits of idealized training extend to categorization of real-world stimuli, namely classifying mammograms as normal or tumorous. Participants in the idealized condition were trained exclusively on items that, according to a norming study, were relatively unambiguous. Participants in the actual condition were trained on a representative range of items. Despite being exclusively trained on easy items, idealized-condition participants were more accurate than those in the actual condition when tested on a range of item types. However, idealized participants experienced difficulties when test items were very dissimilar from training cases. The benefits of idealization, attributable to reducing noise arising from cognitive limitations in memory retrieval, suggest ways to improve real-world decision making.