Implicit Statistical Learning in L2 Sentence Processing: Individual Cognitive Differences.

Abstract

This study investigates whether statistical learning ability, conceptualized as a cognitive ability to learn regularities implicitly, is a good predictor for L2 learners' online language processing performance. Native-English-speaking adults, as a control group, and native-Korean-speaking adult L2 learners of English participated. They completed: (a) an artificial grammar learning task containing nonadjacent dependencies in sequences of non-words, to test statistical learning ability; and (b) a self-paced English reading task containing relative clauses (RC) in which the "filler" and the "gap" formed a long-distance dependency, to test language processing. Both tasks' stimuli were presented element-by-element to mimic the incremental nature of online language processing. The results for the L1 group show that higher accuracy scores on the artificial grammar learning task did not predict higher sentence comprehension scores. The results for the L2 group, however, show a marginally significant correlation between accuracy scores on the artificial grammar learning task and sentence comprehension scores. For both groups, the reading time difference between grammatical and ungrammatical items in the artificial grammar learning task did predict the speed of reading times for items with RCs with a long-distance dependency in the sentence processing task: Larger differences in RTs in the artificial grammar task correlated with slower reading at the critical region of English RCs. These findings suggest a similar mechanism for online first and second language processing of core syntactic phenomena and for statistical learning ability that involves implicitly tracking distributional relations across elements.