Human category inference is mostly independent of the distribution of features within categories
People habitually assign objects to categories based on the objects’ features. In each category, the object features are distributed, meaning that they can vary and correlate across the category members. Past research has found mixed evidence concerning the extent to which people make use of the distribution of features in categories to categorize new objects. To investigate how within-category feature distributions affect people’s categorizations, we collected and analyzed data from two categorization experiments. Participants classified geometrical figures with two features in a trial-by-trial supervised, binary category learning task, followed by an unsupervised transfer task with new feature value combinations. In both experiments, the experimental designs were optimized to compare categorization models that either consider or ignore within-category feature distributions. Experiment 1 used a high-variance category and a low-variance category, and the transfer stimuli fell between the categories. In Experiment 2, both categories had a strong feature correlation, and the transfer stimuli were located in the correlational direction of one category but closer to the members of the other category. Importantly, processing the within-category feature distributions affected how the transfer stimuli would be classified. Our results show that participants’ classifications of the transfer stimuli were in line with ignoring the within-category feature distributions in both experiments. This means that participants (both Ns = 43) assigned the transfer stimuli predominantly to the low-variance category in Experiment 1 (M = 71%) and to the closer category with an incongruent feature correlation in Experiment 2 (M = 88%). Computational cognitive modeling showed that the model which ignores within-category feature distributions described most participants in both experiments with strong evidence (n = 27 in the variance experiment; n = 32 in the correlation experiment), suggesting that people mostly ignore the within-category feature distributions when they categorize new objects. One reason for these findings might be the computational costs involved in estimating the distribution of features in categories.