Under What Conditions Can Recursion be Learned? Effects of Starting Small in Artificial Grammar Learning of Center Embedded Structure

Poletiek, Fenna 
Conway, Christopher 
Ellefson, MR 
Lai, June 
Bocanegra, Bruno 

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It has been suggested that external and/or internal limitations paradoxically may lead to superior learning, i.e., the concepts of starting small and less is more (Elman, 1993; Newport, 1990). In this paper, we explore the type of incremental ordering during training that might help learning, and what mechanism explains this facilitation. We report four artificial grammar learning experiments with human participants. In Experiments 1a and 1b we found a beneficial effect of starting small using two types of simple recursive grammars: right-branching and center-embedding, with recursive embedded clauses in fixed positions and fixed length. This effect was replicated in Experiment 2 (N=100). In Experiment 3 and 4, we used a more complex center-embedded grammar with recursive loops in variable positions, producing strings of variable length. When participants were presented an incremental ordering of training stimuli, as in natural language, they were better able to generalize their knowledge of simple units to more complex units when the training input ‘grew’ according to structural complexity, compared to when it ‘grew’ according to string length. Overall, the results suggest that starting small confers an advantage for learning complex center-embedded structures when the input is organized according to structural complexity.

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Artificial Grammar Learning, Center Embedded Structures, Starting Small, Statistical Learning
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Cognitive Science
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This research has been supported in part by a grant from the Human Frontiers Science Program (grant RGP0177/2001-B) to MHC, and by the Netherlands Organization for scientific Research (NWO) to FHP