Date created: | Last Updated:

Category: Project

Description: An important component of learning to read is the acquisition of letter-to-sound- mappings. The sheer quantity of mappings and many exceptions suggests that children may use a form of statistical learning to acquire them. However, while statistical models of reading are item-based, reading instruction typically focuses on rule-based approaches involving small sets of regularities. This discrepancy poses the question of how different groupings of regularities, an unexamined factor of most reading curricula, may impact learning. Exploring the interplay between item statistics and rules, this study investigated how consonant variability, an item-level factor, and the degree of overlap among the to-be-trained vowel strings, a group-level factor, influence learning. First graders (N = 361) were randomly assigned to be trained on vowel sets with high overlap (e.g., EA, AI) or low overlap (EE, AI); this was crossed with a manipulation of consonant frame variability. While high vowel overlap led to poorer initial performance, it resulted in more learning when tested immediately and after a two-week-delay. There was little beneficial effect of consonant variability. These findings suggest that online letter/sound processing impacts how new knowledge is integrated into existing information. Moreover, they suggest that vowel overlap should be considered when designing reading curricula.