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

Fenna H. Poletiek, Christopher M. Conway, Michelle R. Ellefson, Jun Lai, Bruno R. Bocanegra, Morten H. Christiansen

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

It has been suggested that external and/or internal limitations paradoxically may lead to superior learning, that is, the concepts of starting small and less is more (Elman,; Newport,). 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.

Original languageEnglish (US)
Pages (from-to)2855-2889
Number of pages35
JournalCognitive Science
Volume42
Issue number8
DOIs
StatePublished - Nov 2018

Keywords

  • Artificial grammar learning
  • Center-embedded structures
  • Starting small
  • Statistical learning

ASJC Scopus subject areas

  • Experimental and Cognitive Psychology
  • Cognitive Neuroscience
  • Artificial Intelligence

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