Inference and Eyetracking

In addition to traditional supervised classification learning, people can also learn categories by predicting the features of category members. It has been proposed that feature inference learning promotes the learning of more within-category information and a prototype representation of the category, as compared to classification learning that promotes learning of diagnostic information. We tracked learners' eye movements during inference learning and found (Expt. 1) that they indeed fixated other features (even though those features were not necessary to predict the missing feature), providing the opportunity to extract within-category information. But those fixations were limited to only those features that needed to be predicted on future trials (Expt. 2). In other words, inference learning promotes the acquisition of within-category information not because participants are motivated to learn that information, but rather because of the anticipatory learning it