Skip to main content
Article thumbnail
Location of Repository

Syntactic structure and artificial grammar learning : The learnability of embedded hierarchical structures

By M.H. de Vries, P. Monaghan, S. Knecht and P. Zwitserlood


Embedded hierarchical structures, such as ‘‘the rat the cat ate was brown’’, constitute a core generative property of a natural language theory. Several recent studies have reported learning of hierarchical embeddings in artificial grammar learning (AGL) tasks, and described the functional specificity of Broca’s area for processing such structures. In two experiments, we investigated whether alternative strategies can explain the learning success in these studies. We trained participants on hierarchical sequences, and found no evidence for the learning of hierarchical embeddings in test situations identical to those from other studies in the literature. Instead, participants appeared to solve the task by exploiting surface distinctions between legal and illegal sequences, and applying strategies such as counting or repetition detection. We suggest alternative interpretations for the observed activation of Broca’s area, in terms of the application of calculation rules or of a differential role of working memory. We claim that the learnability of hierarchical embeddings in AGL tasks remains to be demonstrated

Topics: 1203, 2805, 3205, 3310
Year: 2008
DOI identifier: 10.1016/j.cognition.2007.09.002
OAI identifier:

Suggested articles

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.