Systems of correlations in rule and category learning: Use of structured input in learning syntactic categories

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Abstract
Three studies with artificial grammars investigated one property of natural language and one learning procedure which capitalises on this property to aid learning. Learning syntax is difficult because the rules and categories of syntax form a mutually defining system. Structural analogies to other systems (e.g. semantics, interaction) are limited and unreliable. On the other hand, treating syntax as a formal system and applying distributional analysis poses an extremely difficult learning problem. The property investigated is systematicity, coherence among multiple interpredictive features marking syntactic categories. The learning procedure is a method for directing attention to predictive features. This procedure predicts that an individual correlational rule or pattern will be learned more easily when it is part of a system of rules among intercorrelated features than when the identical rule occurs in isolation—complexity facilitates learning. The artificial grammar experiments varied the structure of input; several versions of noun subcategories, analogous to gender or classifiers in natural languages, were used. They tested the prediction that learning an individual rule would be facilitated by other, intercorrelated rules. In all three experiments, the predicted benefit was found. These studies identify one way that the coherence in natural language might be used appropriately to guide and facilitate the learning process.