Probabilistically Cued Patterns Trump Perfect Cues in Statistical Language Learning

Research output: Contribution to journalArticle

Abstract

Probabilistically cued co-occurrence relationships between word categories are common in natural languages but difficult to acquire. For example, in English, determiner-noun and auxiliary-verb dependencies both involve co-occurrence relationships but determiner-noun relationships are more reliably marked by correlated distributional and phonological cues and appear to be learned more readily. We tested whether experience with co-occurrence relationships that are more reliable promotes learning those that are less reliable using an artificial language paradigm. Prior experience with deterministically cued contingencies did not promote learning of less reliably cued structure, nor did prior experience with relationships instantiated in the same vocabulary. In contrast, prior experience with probabilistically cued co-occurrence relationships instantiated in different vocabulary did enhance learning. Thus, experience with co-occurrence relationships sharing underlying structure but not vocabulary may be an important factor in learning grammatical patterns. Furthermore, experience with probabilistically cued co-occurrence relationships, despite their difficultly for naïve learners, lays an important foundation for learning novel probabilistic structure.

Original languageEnglish (US)
Pages (from-to)66-87
Number of pages22
JournalLanguage Learning and Development
Volume9
Issue number1
DOIs
StatePublished - Jan 1 2013

ASJC Scopus subject areas

  • Education
  • Language and Linguistics
  • Linguistics and Language

Fingerprint Dive into the research topics of 'Probabilistically Cued Patterns Trump Perfect Cues in Statistical Language Learning'. Together they form a unique fingerprint.

  • Cite this