Modeling Implicit and Explicit Processes in Recursive Sequence Structure Learning


Recursive structure is viewed as a central property of human language, yet the mechanisms that underlie the acquisition and processing of this structure are subject to intense debate. The artificial grammar learning paradigm has shed light onto syntax acquisition, but has rarely been applied to the more complex, context-free grammars that are needed to represent recursive structure. We adapt the artificial grammar serial reaction time task to study the online acquisition of recursion, and compare human performance to the predictions made by a number of computational language models, chosen to reflect multiple levels and types of syntactic complexity (n-grams, hidden markov models, simple recurrent networks, and Bayesian-induced probabilistic context-free grammars). Evidence is found for a dissociation between explicit and implicit mechanisms of sequence processing, with the SRN more highly correlated with implicit performance, and the PCFG more correlated with explicit awareness of the sequential structure.

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