title: The impact of adjacent-dependencies and staged-input on the learnability of center-embedded hierarchical structures.
abstract: 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.
authors.affiliation: Leiden University, Cognitive Psychology, 2300 RB Leiden, The Netherlands. laij@fsw.leidenuniv.nl
authors.forename: Jun
authors.initials: J
authors.lastname: Lai
authors.forename: Fenna H
authors.initials: FH
authors.lastname: Poletiek
mesh_terms: D005260:Female; D006801:Humans; D007858:Learning; D008037:Linguistics; D008297:Male