Abstract

AbstractThis study addresses whether there is anything special about learning a third language, as compared to learning a second language, that results solely from the order of acquisition. We use a computational model based on the mathematical framework of Linear Discriminative Learning to explore this question for the acquisition of a small trilingual vocabulary, with English as L1, German or Mandarin as L2, and Mandarin or Dutch as L3. Our simulations reveal that when qualitative differences emerge between the learning of a first, second, and third language, these differences emerge from distributional properties of the particular languages involved rather than the order of acquisition per se, or any difference in learning mechanism. One such property is the number of homophones in each language, since within‐language homophones give rise to errors in production. Our simulations also show the importance of suprasegmental information in determining the kinds of production errors made.

Highlights

  • Our computational framework is that of Naive Discriminative Learning (NDL; Baayen, Milin, Filipovic Durdevic, Hendrix, & Marelli, 2011) and its twin, Linear Discriminative Learning (LDL; Baayen, Chuang, Shafaei-Bajestan, & Blevins, 2019)

  • We present a first proposal about how tone can be incorporated into the framework of LDL, and use this to explore the extent to which the tone system of Mandarin, as opposed to the intonational systems of the Germanic languages, influences lexical learning

  • General Discussion Is multilingualism qualitatively different from bilingualism? In this study, we addressed this question by means of a series of simulation studies implementing central concepts of the Discriminative Lexicon theory

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Summary

Introduction

Starting early may be advantageous for mastery of a new language, but. Our computational framework is that of Naive Discriminative Learning (NDL; Baayen, Milin, Filipovic Durdevic, Hendrix, & Marelli, 2011) and its twin, Linear Discriminative Learning (LDL; Baayen, Chuang, Shafaei-Bajestan, & Blevins, 2019). Both NDL and LDL implement discrimination learning, which has a long history in physics (Kalman, 1960; Widrow & Hoff, 1960), statistics (formally, LDL implements multivariate multiple regression), and psychology (Ellis, 2006b; Ramscar, Dye, & McCauley, 2013; Ramscar & Yarlett, 2007; Rescorla & Wagner, 1972; Rescorla, 1988; Siegel & Allan, 1996).

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