Hebart et al. recently analysed 1.5 million human similarity judgments and found that natural objects are described by a small set of interpretable dimensions. Such large-scale analyses offer new opportunities to characterise how people represent their knowledge, but also challenges, including scaling to even larger data sets and integrating accounts of semantic representation. Read Full […]

Abstract We trained a computational model (the Chunk‐Based Learner; CBL) on a longitudinal corpus of child–caregiver interactions in English to test whether one proposed statistical learning mechanism—backward transitional probability—is able to predict children’s speech productions with stable accuracy throughout the first few years of development. We predicted that the model less accurately reconstructs children’s speech […]