The effect of children’s prior knowledge and language abilities on their statistical learning

Abstract Statistical learning (SL) is assumed to lead to long-term memory representations. However, the way that those representations influence future learning remains largely unknown. We studied how children’s existing distributional linguistic knowledge influences their subsequent SL on a serial recall task, in which 49 German-speaking seven- to nine-year-old children repeated a series of six-syllable sequences. These contained either (i) bisyllabic words based on frequently occurring German syllable transitions (naturalistic sequences), (ii) bisyllabic words created from unattested syllable transitions (non-naturalistic sequences), or (iii) random syllable combinations (unstructured foils). Children demonstrated learning from naturalistic sequences from the beginning of the experiment, indicating that their implicit memory traces derived from their input language informed learning from the very early stages onward. Exploratory analyses indicated that children with a higher language proficiency were more accurate in repeating the sequences and improved most throughout the study compared to children with lower proficiency.