Associative chunk strength in artificial grammar learning.

In 4 experiments, adherence to grammatical rules and associative chunk strength (including different measures, each calculated on the basis of the frequency with which bigrams and trigrams present in the test strings appeared in the learning strings) were manipulated independently in the test phase of an artificial grammar learning task. When participants learned few items of the grammar (Experiments 1A and 2A), the associated items were more often classified as grammatical than the nonassociated ones. On the other hand, when the learning phase included most of the grammatical items (Experiments 1B and 2B), the only effect observed was an effect of grammaticality. These results suggest that, depending on the specific constraints of the tasks, knowledge based on bigrams and trigrams and knowledge based on the abstraction of the grammatical structure can be used for the classification task. (PsycINFO Database Record (c) 2010 APA, all rights reserved)