Close encounters of the word kind: Attested distributional information boosts statistical learning

Katja Stärk*, Evan Kidd, REBECCA L.A. FROST

*Corresponding author for this work

Research output: Contribution to journalArticle (journal)peer-review

3 Citations (Scopus)
46 Downloads (Pure)


Statistical learning, the ability to extract regularities from input (e.g., in language), is likely supported by learners’ prior expectations about how component units co-occur. In this study, we investigated how adults’ prior experience with sublexical regularities in their native language influences performance on an empirical language learning task. Forty German-speaking adults completed a speech repetition task in which they repeated eight-syllable sequences from two experimental languages: one containing disyllabic words comprised of frequently occurring German syllable transitions (naturalistic words) and the other containing words made from unattested syllable transitions (non-naturalistic words). The participants demonstrated learning from both naturalistic and non-naturalistic stimuli. However, learning was superior for the naturalistic sequences, indicating that the participants had used their existing distributional knowledge of German to extract the naturalistic words faster and more accurately than the non-naturalistic words. This finding supports theories of statistical learning as a form of chunking, whereby frequently co-occurring units become entrenched in long-term memory.

Original languageEnglish
Pages (from-to)341-373
Number of pages33
JournalLanguage Learning
Issue number2
Early online date10 Oct 2022
Publication statusPublished - Jun 2023


  • Statistical learning
  • serial recall
  • incremental learning
  • long-term memory
  • entrenchment
  • statistical learning


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