Statistical Learning of Nonadjacencies Predicts On-line Processing of Long-Distance Dependencies in Natural Language
نویسندگان
چکیده
Statistical learning (SL) research aims to clarify the potential role that associative-based learning mechanisms may play in language. Understanding learners’ processing of nonadjacent statistical structure is vital to this enterprise, since language requires the rapid tracking and integration of long-distance dependencies. This paper builds upon existing nonadjacent SL work by introducing a novel paradigm for studying SL online. By capturing the temporal dynamics of the learning process, the new paradigm affords insights into the time course of learning and the nature of individual differences. Across 3 interrelated experiments, the paradigm and results thereof are used to bridge knowledge of the empirical relation between SL and language within the context of nonadjacency learning. Experiment 1 therefore charts the micro-level trajectory of nonadjacency learning and provides an index of individual differences in the new task. Substantial differences are further shown to predict participants’ sentence processing of complex, long-distance natural dependencies in Experiment 2. SRN simulations in Experiment 3 then closely capture key patterns of human nonadjacency processing, attesting to the efficacy of associative-based learning mechanisms that appear foundational to performance in the new, language-linked task.
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