Feature inference learning and eyetracking

Besides traditional supervised classification learning, people can learn categories by inferring the missing features of category members. It has been proposed that feature inference learning promotes learning a category’s internal structure (e.g., its typical features and interfeature correlations) whereas classification promotes the learning of diagnostic information. We tracked learners’ eye movements and found in Experiment 1 that inference learners indeed fixated features that were unnecessary for inferring the missing feature, behavior consistent with acquiring the categories’ internal structure. However, Experiments 3 and 4 showed that fixations were generally limited to features that needed to be predicted on future trials. We conclude that inference learning induces both supervised and unsupervised learning of category-to-feature associations rather than a general motivation to learn the internal structure of categories.