Individual behavior in learning of an artificial grammar

Abstract

Artificial grammar learning (AGL) is a widely used experimental paradigm that investigates how syntactic structures are processed. After a familiarization phase, participants have to distinguish strings consistent with a set of grammatical rules from strings that violate these rules. Many experiments report performance solely at a group level and as the total number of correct judgments. This report describes a systematic approach for investigating individual performance and a range of different behaviors. Participants were exposed to strings of the nonfinite grammar A( n )B( n ). To distinguish grammatical from ungrammatical strings, participants had to pay attention to local dependencies while comparing the number of stimuli from each class. Individual participants showed substantially different behavioral patterns despite exposure to the same stimuli. The results were replicated across auditory and visual sensory modalities. It is suggested that an analysis that looks at individual differences grants new insights into the processes involved in AGL. It also provides a solid basis from which to investigate sequence-processing abilities in special populations, such as patients with neurological lesions.