When the “Tabula” is Anything but “Rasa:” What Determines Performance in the Auditory Statistical Learning Task?

Amit Elazar*, Raquel G. Alhama, Louisa Bogaerts, Noam Siegelman, Cristina Baus, Ram Frost

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

How does prior linguistic knowledge modulate learning in verbal auditory statistical learning (SL) tasks? Here, we address this question by assessing to what extent the frequency of syllabic co-occurrences in the learners’ native language determines SL performance. We computed the frequency of co-occurrences of syllables in spoken Spanish through a transliterated corpus, and used this measure to construct two artificial familiarization streams. One stream was constructed by embedding pseudowords with high co-occurrence frequency in Spanish (“Spanish-like” condition), the other by embedding pseudowords with low co-occurrence frequency (“Spanish-unlike” condition). Native Spanish-speaking participants listened to one of the two streams, and were tested in an old/new identification task to examine their ability to discriminate the embedded pseudowords from foils. Our results show that performance in the verbal auditory SL (ASL) task was significantly influenced by the frequency of syllabic co-occurrences in Spanish: When the embedded pseudowords were more “Spanish-like,” participants were better able to identify them as part of the stream. These findings demonstrate that learners’ task performance in verbal ASL tasks changes as a function of the artificial language's similarity to their native language, and highlight how linguistic prior knowledge biases the learning of regularities.

Original languageAmerican English
Article numbere13102
JournalCognitive Science
Volume46
Issue number2
DOIs
StatePublished - Feb 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2022 The Authors. Cognitive Science published by Wiley Periodicals LLC on behalf of Cognitive Science Society (CSS).

Keywords

  • Prior knowledge
  • Speech segmentation
  • Statistical learning
  • Syllable frequency

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