Learning Non-Adjacent Dependencies in Continuous Presentation of an Artificial Language

Abstract

Many grammatical dependencies in natural language involve elements that are not adjacent, such as between the subject and verb in the child always runs. To date, most experiments showing evidence of learning non-adjacent dependencies have used artificial languages in which the to-be-learned dependencies are presented in isolation by presenting the minimal sequences that contain the dependent elements. However, dependencies in natural language are not typically isolated in this way. In this study we exposed learners to non-adjacent dependencies in long sequences of words. We accelerated the speed of presentation and learners showed evidence for learning of non-adjacent dependencies. The previous pause-based positional mechanisms for learning of non-adjacent dependency are challenged.


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