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Implicit learning of recursive context-free grammars

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posted on 2023-06-08, 16:18 authored by Martin Rohrmeier, Qiufang Fu, Zoltan DienesZoltan Dienes
Context-free grammars are fundamental for the description of linguistic syntax. However, most artificial grammar learning experiments have explored learning of simpler finite-state grammars, while studies exploring context-free grammars have not assessed awareness and implicitness. This paper explores the implicit learning of context-free grammars employing features of hierarchical organization, recursive embedding and long-distance dependencies. The grammars also featured the distinction between left- and right-branching structures, as well as between centre- and tail-embedding, both distinctions found in natural languages. People acquired unconscious knowledge of relations between grammatical classes even for dependencies over long distances, in ways that went beyond learning simpler relations (e.g. n-grams) between individual words. The structural distinctions drawn from linguistics also proved important as performance was greater for tail-embedding than centre-embedding structures. The results suggest the plausibility of implicit learning of complex context-free structures, which model some features of natural languages. They support the relevance of artificial grammar learning for probing mechanisms of language learning and challenge existing theories and computational models of implicit learning.

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Publication status

  • Published

File Version

  • Published version

Journal

PLoS ONE

ISSN

1932-6203

Publisher

Public Library of Science

Issue

10

Volume

7

Article number

e45885

Department affiliated with

  • Psychology Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2013-11-13

First Open Access (FOA) Date

2013-11-13

First Compliant Deposit (FCD) Date

2013-11-13

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