Effects of Grammar Complexity on Artificial Grammar Learning

By van den Bos, Esther; Poletiek, Fenna H. | Memory & Cognition, September 2008 | Go to article overview

Effects of Grammar Complexity on Artificial Grammar Learning


van den Bos, Esther, Poletiek, Fenna H., Memory & Cognition


The present study identified two aspects of complexity that have been manipulated in the implicit learning literature and investigated how they affect implicit and explicit learning of artificial grammars. Ten finite state grammars were used to vary complexity. The results indicated that dependency length is more relevant to the complexity of a structure than is the number of associations that have to be learned. Although implicit learning led to better performance on a grammaticality judgment test than did explicit learning, it was negatively affected by increasing complexity: Performance decreased as there was an increase in the number of previous letters that had to be taken into account to determine whether or not the next letter was a grammatical continuation. In particular, the results suggested that implicit learning of higher order dependencies is hampered by the presence of longer dependencies. Knowledge of first-order dependencies was acquired regardless of complexity and learning mode.

When people are presented with exemplars of a simple structure, they may intentionally try to grasp the regularities and become aware of the knowledge they acquire. In addition to this explicit way of learning, Reber (1976, 1989) proposed that structures can also be learned implicitly, without any intention to learn and without complete awareness of the acquired knowledge. These two ways of structure learning are associated with different levels of complexity. For one thing, a minimum level of complexity of the stimuli is required to observe implicit learning (Reber, 1976). This was demonstrated by an experiment in which participants had to obtain target output values by providing input values to unknown equations. The participants acquired explicit knowledge of simple equations, relating each output value to one input value, whereas they acquired implicit knowledge of complex equations, which related each output value to two input values (Lee, 1995).

In addition, Reber (1976) suggested that implicit learning is especially suited to complex structures, whereas explicit learning works well with simple structures but would be hampered by increasing complexity. In the view of Hayes and Broadbent (1988), explicit learning is restricted to simple regularities because the process relies on working memory. Since working memory capacity is limited, explicit learning would involve active selection of a small amount of relevant information. Regularities involving a large number of variables could be acquired only by implicit learning, which would unselectively store the frequency of co-occurrence of all the elements present.

In line with these suggestions, Reber (1976) demonstrated that a finite state grammar could be learned better implicitly than explicitly. In the induction phase of this artificial grammar learning (AGL) experiment, the implicit learning group was instructed to memorize letter strings without being informed that they were generated by a grammar. The explicit learning group received the additional instruction to look for rules underlying the strings. Subsequently, when all the participants had been informed about the existence of the grammar, they were instructed to judge whether or not new strings were grammatical. The participants who had memorized the letter strings were correct more often than the participants who had looked for rules. In addition, Mathews et al. (1989) showed that although a finite state grammar could be learned by performing a memorize task, a rule search task produced better results with a biconditional grammar. Johnstone and Shanks (2001) replicated this finding and suggested that the different results for the two types of grammar are due to differences in complexity. They argued that biconditional grammars are less complex than finite state grammars, since they consist of a smaller number of rules.

Apart from the general observation that implicit learning is better suited to complex structures, whereas explicit learning is more efficient for simple materials, however, the effects of complexity on structure learning have received little attention. …

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Effects of Grammar Complexity on Artificial Grammar Learning
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