Simultaneous training on overlapping grapheme phoneme correspondences augments learning and retention

Abstract An important component of learning to read is the acquisition of letter-to-sound mappings. The sheer quantity of mappings and many exceptions in opaque languages such as English suggests that children may use a form of statistical learning to acquire them. However, whereas 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 affect 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. English-speaking first graders (N = 361) were randomly assigned to be trained on vowel sets with high overlap (e.g., EA, AI) or low overlap (e.g., EE, AI); this was crossed with a manipulation of consonant frame variability. Whereas high vowel overlap led to poorer initial performance, it resulted in more learning when tested immediately and after a 2-week-delay. There was little beneficial effect of consonant variability. These findings indicate that online letter/sound processing affects how new knowledge is integrated into existing information. Moreover, they suggest that vowel overlap should be considered when designing reading curricula.