When global structure " Explains Away" local grammar: A Bayesian account of rule-induction in tone sequences

Colin Dawson, Louann Gerken

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

While many constraints on learning must be relatively experience-independent, past experience provides a rich source of guidance for subsequent learning. Discovering structure in some domain can inform a learner's future hypotheses about that domain. If a general property accounts for particular sub-patterns, a rational learner should not stipulate separate explanations for each detail without additional evidence, as the general structure has " explained away" the original evidence. In a grammar-learning experiment using tone sequences, manipulating learners' prior exposure to a tone environment affects their sensitivity to the grammar-defining feature, in this case consecutive repeated tones. Grammar-learning performance is worse if context melodies are " smooth" - when small intervals occur more than large ones - as Smoothness is a general property accounting for a high rate of repetition. We present an idealized Bayesian model as a " best case" benchmark for learning repetition grammars. When context melodies are Smooth, the model places greater weight on the small-interval constraint, and does not learn the repetition rule as well as when context melodies are not Smooth, paralleling the human learners. These findings support an account of abstract grammar-induction in which learners rationally assess the statistical evidence for underlying structure based on a generative model of the environment.

Original languageEnglish (US)
Pages (from-to)350-359
Number of pages10
JournalCognition
Volume120
Issue number3
DOIs
Publication statusPublished - Sep 2011

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Keywords

  • Bayesian modeling
  • Language acquisition
  • Music cognition
  • Rule-learning
  • Statistical learning

ASJC Scopus subject areas

  • Linguistics and Language
  • Cognitive Neuroscience
  • Experimental and Cognitive Psychology

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