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Studying Frequency Effects in Learning Center-embedded Recursion

Studying Frequency Effects in Learning Center-embedded Recursion Jun Lai (J.Lai@ tilburguniversity.edu) Tilburg Center for Logic and Philosophy of Science; Tilburg Center for Cognition and Communication, Tilburg University, the Netherlands Emiel Krahmer (E.J.Krahmer@ tilburguniversity.edu) Tilburg Center for Cognition and Communication, Tilburg University, the Netherlands Jan Sprenger (j.sprenger@tilburguniversity.edu) Tilburg Center for Logic and Philosophy of Science, Tilburg University, the Netherlands Abstract 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. Keywords: center-embedded recursion; statistical learning; starting small; repetition; frequency distribution Introduction In early language acquisition, children display remarkable capabilities in learning and producing new sentences. One of the most discouraging obstacles in language learning lies in making associations between objects and actions (Kersten & Smith, 2002). Children tend to initially concentrate on objects and agents, rather than on the relations between them (Behrend, 1990). One of the most difficult sentential relations to learn is center-embedded (CE) recursion, which is proposed to be a crucial factor distinguishing humans and nonhumans (Corballis, 2007; Hauser, Chomsky, & Fitch, 2002). For instance, “The rabbit that the fox chased ran away.” is a typical CE recursive sentence, with one object-action pair (fox-chased), inserted as a relative clause in the middle of another pair (the rabbit-ran away) that forms the main sentence. The complexity of the sentence poses great challenges for sentence processing and understanding, since human parsers not only need to store all the relevant information in memory, but also need to integrate the associated elements, in order to detect who did what to whom. There is a large amount of research attempting to explain why the relations in CE recursion are so notoriously difficult to comprehend (Christiansen & MacDonald, 2009). For instance, Chomsky (1965) argued that the way that CE recursion is constructed requires human parsers to associate related elements, which is complicated for remote dependencies. Moreover, Johnson (1998) and Morrill (2000) indicated that the comprehension collapses at the moment when too many dependencies are waiting to be paired in memory. Though processing CE recursion seems extraordinarily challenging, it has been shown that acquisition of such complicated structures could be enhanced to a certain extent by varying the learning sample, for instance, when the complexity of the learning material is arranged incrementally (Conway, Ellefson, & Christiansen, 2003; Elman, 1993; Lai & Poletiek, 2011). It has also been shown that the high variability of the intervening middle element in a non-adjacent structure could result in better understanding and discrimination of the remote dependencies (Gomez, 2002). Furthermore, the frequency distribution of the co-occurrence of related dependencies has been observed to affect the categorization of the intervening elements (Mintz, 2003). In addition, it has been shown that people could master the sentential structures easier when verbs were in high frequencies (Kidd, Lieven, & Tomasello, 2010). Thus, a skewed frequency distribution, which could result in different constructions of the sample, e.g. size and probability, is also an aid in learning complex structures. With regard to the effects on sample diversity on facilitating learning, there are primarily two opposite points of view: more is better vs. fewer is better (see Maguire, Hirsh-Pasek, Golinkoff, & Brandone, 2008, for a review). The first perspective assumes that the variety of learning samples helps