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WHY ARE LEXICAL TONES DIFFICULT TO LEARN?

INSIGHTS FROM THE INCIDENTAL LEARNING OF TONE-SEGMENT CONNECTIONS

Published online by Cambridge University Press:  16 September 2019

Ricky KW Chan*
Affiliation:
University of Hong Kong
Janny HC Leung
Affiliation:
University of Hong Kong
*
*Correspondence concerning this article should be addressed to Ricky Chan, Speech, Language and Cognition Laboratory, School of English, University of Hong Kong, Pokfulam Road, Hong Kong. E-mail: rickykwc@hku.hk
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Abstract

L2 sounds present different kinds of challenges to learners at the phonetic, phonological, and lexical levels, but previous studies on L2 tone learning mostly focused on the phonetic and lexical levels. The present study employs an innovative technique to examine the role of prior tonal experience and musical training on forming novel abstract syllable-level tone categories. Eighty Cantonese and English musicians and nonmusicians completed two tasks: (a) AX tone discrimination and (b) incidental learning of artificial tone-segment connections (e.g., words beginning with an aspirated stop always carry a rising tone) with synthesized stimuli modeled on Thai. Although the four participant groups distinguished the target tones similarly well, Cantonese speakers showed abstract and implicit knowledge of the target tone-segment mappings after training but English speakers did not, regardless of their musical experience. This suggests that tone language experience, but not musical experience, is crucial for forming novel abstract syllable-level tone categories.

Information

Type
Research Article
Copyright
Copyright © Cambridge University Press 2019 
Figure 0

TABLE 1. Background information of each participant group.

Figure 1

TABLE 2. Definitions of “hit,” “miss,” “false alarm,” and “correct rejection” for participants’ responses in the AX discrimination task.

Figure 2

FIGURE 1. Sensitivity (d') (left) and response bias (c) (right) of the four participant groups in the AX discrimination task.

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TABLE 3. Post-hoc Tukey pairwise multiple comparisons for the response bias (c) of the four participant groups

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FIGURE 2. Accuracy and log-transformed reaction time of the four groups for the AB pairs in the AX discrimination task (C = Cantonese, E = English; error bar = 1 SE).

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TABLE 4. Summary of the statistics of the mixed model comparisons for the factors of interest for the AX discrimination task (* = significant effect).

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FIGURE 3. Accuracy of the four groups for the critical items and extension items in the pronunciation judgment task (C = Cantonese, E = English; error bar = 1 SE).

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TABLE 5. Summary of the statistics of the mixed model comparisons for the factors of interest for the pronunciation judgment task (* = significant effect).

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TABLE 6. Accuracy (% correct) on and 95% confidence intervals (from upper bound to lower bound) of the critical items for the prediction of each participant group based on GLMM (chance level = 50%).

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TABLE 7. Accuracy (% correct) on and 95% confidence intervals (from upper bound to lower bound) of the extension items for the prediction of each participant group based on GLMM (chance level = 50%).

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TABLE 8. Summary of the statistics of the mixed model comparisons on item types in the pronunciation judgment task (* = significant effect).

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TABLE 9. Number and percentage of responses of each attribution for critical items and extension items (36 critical/extension items * 40 participants = 1,440 responses).

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TABLE 10. Accuracy (% correct) on and 95% confidence intervals (from upper bound to lower bound) of the critical items for the prediction of Cantonese participants (with data from their implicit attributions only) based on GLMM (chance level = 50%).

Figure 13

TABLE 11. Accuracy (% correct) on and 95% confidence intervals (from upper bound to lower bound) of the extension items for the prediction of Cantonese participants (with data from their implicit attributions only) based on GLMM (chance level = 50%).