Proceedings of the Annual Meeting of the Cognitive Science Society

Humans can extract co-occurrence regularities from their environment, and use them for learning. This statistical learningability (SL) has been studied extensively. However, almost all SL studies present the regularities to be learned in uniformfrequency distributions (each unit appears equally often). In contrast, real-world learning environments, including thewords children hear and the objects they see, are not uniform, and consequently more predictable than lab-based ones.Recent research shows that word segmentation in children and adults is facilitated after exposure to a Zipfian distribution.Here, we ask if this effect is domain-general by testing children and adults on a visual SL task. Both children and adultsperformed better in the Zipfian distribution compared to the uniform one, overall, and for low-frequency triplets. Theseresults illustrate the impact of distribution predictability on learning across modality and age, and point to the possiblelearnability advantage of skewed distributions in the real-world.