Can Rational Models Be Good Accounts of Developmental Change? The Case of Language Development at Two Time Scales

Colin R. Dawson, Louann Gerken

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Rational models of human perception and cognition have allowed researchers new ways to look at learning and the ability to make inferences from data. But how good are such models at accounting for developmental change? In this chapter, we address this question in the domain of language development, focusing on the speed with which developmental change takes place, and classifying different types of language development as either fast or slow. From the pattern of fast and slow development observed, we hypothesize that rational learning processes are generally well suited for handling fast processes over small amounts of input data. In contrast, we suggest that associative learning processes are generally better suited to slow development, in which learners accumulate information about what is typical of their language over time. Finally, although one system may be dominant for a particular component of language learning, we speculate that both systems frequently interact, with the associative system providing a source of emergent hypotheses to be evaluated by the rational system and the rational system serving to highlight which aspects of the learner's input need to be processed in greater depth by the associative system.

Original languageEnglish (US)
Pages (from-to)95-124
Number of pages30
JournalAdvances in Child Development and Behavior
Volume43
DOIs
StatePublished - 2012

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Keywords

  • Bayesian learning
  • Hebbian learning
  • Language development
  • Phonetics
  • Phonology
  • Syntax
  • Word learning

ASJC Scopus subject areas

  • Developmental and Educational Psychology
  • Behavioral Neuroscience
  • Pediatrics, Perinatology, and Child Health

Cite this

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abstract = "Rational models of human perception and cognition have allowed researchers new ways to look at learning and the ability to make inferences from data. But how good are such models at accounting for developmental change? In this chapter, we address this question in the domain of language development, focusing on the speed with which developmental change takes place, and classifying different types of language development as either fast or slow. From the pattern of fast and slow development observed, we hypothesize that rational learning processes are generally well suited for handling fast processes over small amounts of input data. In contrast, we suggest that associative learning processes are generally better suited to slow development, in which learners accumulate information about what is typical of their language over time. Finally, although one system may be dominant for a particular component of language learning, we speculate that both systems frequently interact, with the associative system providing a source of emergent hypotheses to be evaluated by the rational system and the rational system serving to highlight which aspects of the learner's input need to be processed in greater depth by the associative system.",
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