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SECOND AND THIRD LANGUAGE LEARNERS’ SENSITIVITY TO JAPANESE PITCH ACCENT IS ADDITIVE

AN INFORMATION-BASED MODEL OF PITCH PERCEPTION

Published online by Cambridge University Press:  25 March 2019

Seth Wiener*
Affiliation:
Carnegie Mellon University
Seth Goss
Affiliation:
Emory University
*
*Correspondence concerning this article should be addressed to Seth Wiener, Department of Modern Languages, Carnegie Mellon University, 160 Baker Hall, 5000 Forbes Avenue, Pittsburgh, PA 15213. E-mail: sethw1@cmu.edu
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Abstract

This study examines second (L2) and third (L3) language learners’ pitch perception. We test the hypothesis that a listener’s discrimination of and sensitivity (d’) to Japanese pitch accent reflects how pitch cues inform all words a listener knows in an additive, nonselective manner rather than how pitch cues inform words in a selective, Japanese-only manner. Six groups of listeners performed a speeded ABX discrimination task in Japanese. Groups were defined by their L1, L2, and L3 experience with the target language’s pitch cues (Japanese), a language with less informative pitch cues (English), or a language with more informative pitch cues (Mandarin Chinese). Results indicate that sensitivity to pitch is better modeled as a function of pitch’s informativeness across all languages a listener speaks. These findings support cue-centric views of perception and transfer, demonstrate potential advantageous transfer of tonal-L1/L2 speakers, and highlight the cumulative role that pitch plays in language learning.

Information

Type
Research Report
Open Practices
Open materials
Copyright
Copyright © Cambridge University Press 2019 
Figure 0

TABLE 1. Participant information (group means)

Figure 1

TABLE 2. Estimates of pitch informativeness and Japanese experience

Figure 2

FIGURE 1. ABX discrimination accuracy by group. Black box indicates 95% confidence interval. White line within interval indicates group mean.

Figure 3

TABLE 3. Fixed-effect terms in mixed-effects model of the likelihood of ABX discrimination accuracy

Figure 4

FIGURE 2. Linear regression model fits (with SE).

Supplementary material: File

Wiener and Goss supplementary material

Table S1

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