Studying Frequency Effects in Learning Center-embedded Recursion

Long-distance dependencies in center-embedded recursion are among the most typical but also most difficult structures in human language (Corballis, 2007; Hauser, Chomsky, & Fitch, 2002). Concerning the impact of the learning sample on grasping object-action relations, there are two opposing arguments: more is better vs. fewer is better (Maguire, Hirsh-Pasek, Golinkoff, & Brandone, 2008). The former theory assumes that a large number of different exemplars facilitates learning (Gentner, 2003), while the latter theory suggests that a more restricted set of unique exemplars with repetitions advances the learning of these patterns (Casasola, 2005; Kersten & Smith, 2002). In the current study, we designed a grammaticality-judgment task and test both theories using an artificial grammar learning paradigm. We found that when participants were trained on fewer unique exemplars, but with repetitions, they could still perform significantly better than at chance level. Moreover, when the few unique exemplars were repeated for an unequal number of times, their performance was boosted to a higher level. In line with the fewer is better theory, our findings point to a repetition effect and frequency distribution effect in processing hierarchical center-embedded recursion.