When ‘More’ in Statistical Learning Means ‘Less’ in Language: Individual Differences in Predictive Processing of Adjacent Dependencies

Although statistical learning (SL) is widely assumed to play a key role in language, few empirical studies aim to directly and systematically link variation across SL and language. In this study, we build on prior work linking differences in nonadjacent SL to on-line language, by examining individualdifferences in adjacent SL. Experiment 1 documents the trajectory of adjacency learning and establishes an individualdifferences index for statistical bigram learning. Experiment 2 probes for within-subjects associations between adjacent SL and on-line sentence processing in three different contexts (involving embedded subject-object relative-clauses, thematic fit constraints in reduced relative-clause ambiguities, and subject-verb agreement). The findings support the notion that proficient adjacency skills can lead to an over-attunement towards computing local statistics to the detriment of more efficient processing patterns for nonlocal language dependencies. Finally, the results are discussed in terms of questions regarding the proper relationship between adjacent and nonadjacent SL mechanisms.