The impact of adjacent-dependencies and staged-input on the learnability of center-embedded hierarchical structures

Cognition. 2011 Feb;118(2):265-73. doi: 10.1016/j.cognition.2010.11.011. Epub 2010 Dec 9.


A theoretical debate in artificial grammar learning (AGL) regards the learnability of hierarchical structures. Recent studies using an A(n)B(n) grammar draw conflicting conclusions (Bahlmann & Friederici, 2006; De Vries, Monaghan, Knecht, & Zwitserlood, 2008). We argue that 2 conditions crucially affect learning A(n)B(n) structures: sufficient exposure to zero-level-of-embedding (0-LoE) exemplars and a staged-input. In 2 AGL experiments, learning was observed only when the training set was staged and contained 0-LoE exemplars. Our results might help understanding how natural complex structures are learned from exemplars.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Female
  • Humans
  • Learning*
  • Linguistics*
  • Male