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BRIEF RESEARCH REPORT article

Front. Educ.
Sec. Language, Culture and Diversity
Volume 9 - 2024 | doi: 10.3389/feduc.2024.1393379
This article is part of the Research Topic Tonal Language Processing and Acquisition in Native and Non-native Speakers View all 4 articles

Learning Lexical Tone through Statistical Learning in Non-Tone Language Speakers

Provisionally accepted
Mi Tang Mi Tang *Jennifer K. Spenader Jennifer K. Spenader Stephen Jones Stephen Jones
  • Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, Faculty of Science and Engineering, University of Groningen, Groningen, Netherlands

The final, formatted version of the article will be published soon.

    This study investigates the role of statistical learning in the learning of lexical tones by nontone language speakers. Over two experiments, participants were exposed to tone-syllable combinations with conditioned patterns. Experiment 1 used a typical statistical learning paradigm without feedback to assess participants' ability to discriminate tone-syllable combinations. The results revealed significant syllable learning but not tone learning. Experiment 2 controlled for syllable occurrence to isolate the learning of tonal patterns and demonstrated above-chance learning accuracy from the first training day, indicating successful lexical tone learning through the statistical learning mechanisms. The findings suggest that statistical learning without feedback facilitates lexical tone learning. Our study not only supports the universality of statistical learning in language acquisition but also prompts further research into its application in educational settings for teaching tonal languages.

    Keywords: Lexical tone learning, statistical learning, lexical tone, non-native phonetic learning, Tonal language

    Received: 29 Feb 2024; Accepted: 24 Jul 2024.

    Copyright: © 2024 Tang, Spenader and Jones. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Mi Tang, Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, Faculty of Science and Engineering, University of Groningen, Groningen, Netherlands

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.