Do the mechanisms underlying language in fact serve general-purpose functions that preexist this uniquely human capacity? To address this contentious and empirically challenging issue, we systematically tested the predictions of a well-studied neurocognitive theory of language motivated by evolutionary principles. Multiple metaanalyses were performed to examine predicted links between language and two general-purpose learning systems, declarative and procedural memory. The results tied lexical abilities to learning only in declarative memory, while grammar was linked to learning in both systems in both child first language and adult second language, in specific ways. In second language learners, grammar was associated with only declarative memory at lower language experience, but with only procedural memory at higher experience. The findings yielded large effect sizes and held consistently across languages, language families, linguistic structures, and tasks, underscoring their reliability and validity. The results, which met the predicted pattern, provide comprehensive evidence that language is tied to general-purpose systems both in children acquiring their native language and adults learning an additional language. Crucially, if language learning relies on these systems, then our extensive knowledge of the systems from animal and human studies may also apply to this domain, leading to predictions that might be unwarranted in the more circumscribed study of language. Thus, by demonstrating a role for these systems in language, the findings simultaneously lay a foundation for potentially important advances in the study of this critical domain.

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Williams’s (2005) study on “learning without awareness” and three subsequent extensions (Faretta-Stutenberg & Morgan-Short, 2011; Hama & Leow, 2010; Rebuschat, Hamrick, Sachs, Riestenberg, & Ziegler, 2013) have reported conflicting results, perhaps in part due to differences in how awareness has been measured. The present extension of Williams (2005) addresses this possibility directly by triangulating data from three awareness measures: concurrent verbal reports (think-aloud protocols), retrospective verbal reports (postexposure interviews), and subjective measures (confidence ratings and source attributions). Participants were exposed to an artificial determiner system under incidental learning conditions. One experimental group thought aloud during training, another thought aloud during training and testing, and a third remained silent, as did a trained control group. All participants were then tested by means of a forced-choice task to establish whether learning took place. In addition, all participants provided confidence ratings and source attributions on test items and were interviewed following the test. Our results indicate that, although all experimental groups displayed learning effects, only the silent group was able to generalize the acquired knowledge to novel instances. Comparisons of concurrent and retrospective verbal report data shed light on the conflicting findings previously reported in the literature and highlight important methodological issues in implicit and explicit learning research.

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Humans are remarkably sensitive to the statistical structure of language. However, different mechanisms have been proposed to account for such statistical sensitivities. The present study compared adult learning of syntax and the ability of two models of statistical learning to simulate human performance: Simple Recurrent Networks, which learn by predictive computation, and PARSER, which learns chunks as a byproduct of general principles of associative learning and memory. In the first stage, a semiartificial language paradigm was used to gather human data. In the second stage, a simulation paradigm was then used to compare the patterns of performance of the SRN and PARSER. After the human adults and the computational models were trained on sentences from the semiartificial language with probabilistic syntax, their learning outcomes were compared. Neither model was able to fully reproduce the human data, which may indicate less robust statistical learning effects in adults; however, PARSER was able to simulate more of the adult learning data than the SRN, suggesting a possible role for chunk formation in early phases of adult learning of second language syntax.