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 s trings appeared in the learning strings) were manipulated independentl y in the test phase of an artificial grammar learning task. When parti cipants learned few items of the grammar (Experiments 1A and 2A), the associated items were more often classified as grammatical than the no nassociated 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 o n bigrams and trigrams and knowledge based on the abstraction of the g rammatical structure can be used for the classification task.