Abstract
In an artificial grammar learning study, Lai & Poletiek (2011) found that human participants could learn a center-embedded recursive grammar only if the input during training was presented in a staged fashion. Previous studies on artificial grammar learning, with randomly ordered input, failed to demonstrate learning of such a center-embedded structure. In the account proposed here, the staged input effect is explained by a fine-tuned match between the statistical characteristics of the incrementally organized input and the development of human cognitive learning over time, from low level, linear associative, to hierarchical processing of long distance dependencies. Interestingly, staged input seems to be effective only for learning hierarchical structures, and unhelpful for learning linear grammars.
Highlights
Language acquisition is one of the most complex tasks imaginable
The non-linear process required for natural language seems hard to explain with statistical learning mechanisms
(and less recently; see Gold 1967), it has been proposed that this type of hierarchical structures is unique for human language and is a crucial characteristic of the human language faculty (Hauser et al 2002, Fitch & Hauser 2004; see Corballis 2007)
Summary
Language acquisition is one of the most complex tasks imaginable. Young learners, from infancy on, are faced with a noisy, degraded, and small set of streams of sounds — linguistic stimuli — from which grammatical principles have to be induced. Though generalization from the stimuli is needed to learn the grammar, it is bound to complex constraints: It should not go too far, and not be simple and linear. It is one of the most persistent mysteries in cognitive science how humans achieve this goal. The artificial grammar learning paradigm can be used to perform a laboratory test of possible effects of environmental characteristics on the learnability of sequential structures, by simulating these characteristics in the experimental task and comparing learning behavior under experimental conditions with a matched control condition in which the investigated characteristics are not implemented
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