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Interlingual two-to-one mapping of tonal categories*

Published online by Cambridge University Press:  12 May 2016

JUNRU WU*
Affiliation:
Dept. Chinese Language and Literature, East China Normal University Leiden University Centre for Linguistics Leiden Institute for Brain and Cognition
YIYA CHEN
Affiliation:
Leiden University Centre for Linguistics Leiden Institute for Brain and Cognition
VINCENT J. VAN HEUVEN
Affiliation:
Leiden University Centre for Linguistics Leiden Institute for Brain and Cognition Dept. Applied Linguistics, University of Pannonia
NIELS O. SCHILLER
Affiliation:
Leiden University Centre for Linguistics Leiden Institute for Brain and Cognition
*
Address for correspondence: Junru Wu, Dept. Chinese Language and Literature, East China Normal University, 500 Dongchuan Rd., Shanghai 200241, Chinajrwu@zhwx.ecnu.edu.cn
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Abstract

Both Standard Chinese (SC) high- and low-rising tones sound like the rising tone in Jinan Mandarin (JM) Chinese. Acoustically (Experiment 1), the JM rising tone overlaps with both SC rising tones, but more with the high-rising tone than with the low-rising tone. Perceptually (Experiment 2), the JM rising tone was more likely identified as the SC high-rising tone by SC monolinguals. Experiment 3 examined the role of this two-to-one interlingual tonal mapping in bilingual lexical access. Final high-rising SC pseudo-words were more frequently and more quickly accepted as JM real words than final low-rising SC pseudo-words were. However, both high- and low-rising SC pseudo-words triggered equivalent facilitatory semantic priming on JM real-word targets. The results suggest that different tones are represented in the bilinguals’ mental lexicon in terms of fine-grained and sometimes overlapping acoustic specifications. Lexical activation and semantic activation are partially independent.

Information

Type
Research Article
Copyright
Copyright © Cambridge University Press 2016 
Figure 0

Figure 1. Upper panels: pitch contours on the rimes of a SC monosyllabic tonal minimal set with the same segmental structure /ʨiɛ/ [from left to right, 1: high-level, 2: high-rising, 3: low-rising (dipping); 4: falling]. It is pronounced by a young female SC native speaker with PSC 1b. Examples of SC pitch contours plotted with early recordings can be found by Brotzman (1964), Dreher & Lee (1968), and Liu (1924). Lower panels: Pitch contours on the rimes of a JM monosyllabic tonal minimal set with the same segmental structure /ʨiɛ/ [from left to right, 1: rising, 2: high-falling, 3: high-level; 4: low-falling], pronounced by an old male JM speaker recorded by Qian (1998). The plots were made with raw data (dotted line) and a custom-made piecewise regression function (dashed line) in Praat (Boersma & Weenink, 2014). The dotted lines represent the pitch contour.

Figure 1

Table 1. Coefficients for the linear predictors in the generalized additive model fitted to Pitch of JM & SC production data (Experiment 1, ** p < .01, *** p < .001).

Figure 2

Table 2. Coefficients for the smooth terms in the generalized additive model fitted to Pitch of JM & SC production data (Experiment 1,*** p < .001).

Figure 3

Figure 2. Scattered contour plots (upper panels, Experiment 1 & 2) and estimated smoothes (lower panels, Experiment 1) for the final-rising JM words (JM rising: red/yellow), final-high-rising SC pseudo-words (SC high-rising, green), and final-low-rising SC pseudo-words (SC low-rising, blue). In the scattered contour plots, JM R contours are coloured from red to yellow, according to the data from Experiment 2. The more likely a contour was identified as SC high-rising, the redder it is; the more likely it was identified as SC low-rising the yellower it is.

Figure 4

Table 3. Coefficients for the linear predictors in the generalized additive model fitted to Pitch of JM production-identification data (Experiment 2, **p < .01, *** p < .001).

Figure 5

Table 4. Coefficients for the smooth terms in the generalized additive model fitted to Pitch of JM production-identification data (Experiment 2, *** p < .001).

Figure 6

Figure 3. Upper panels: estimated smoothes (left) and the corresponding difference curve (right) for JM final-rising words (JM rising + rising) identified as SC final-high-rising (SC high-rising + high-rising) and final-low-rising (SC high-rising + low-rising) pseudo-words. Lower panels: estimated smoothes (left) and the corresponding difference curve (right) for SC final-high-rising (SC high-rising + high-rising) and final-low-rising (SC high-rising + low-rising) pseudo-words.

Figure 7

Figure 4. Upper panels: estimated smoothes (left) and the corresponding difference curve (right) for JM final-rising words (JM high-level + rising) identified as SC final-high-rising (SC high-level + high-rising) and final-low-rising (SC high-level + low-rising) pseudo-words. Lower panels: estimated smoothes (left) and the corresponding difference curve (right) for SC final-high-rising (SC high-level + high-rising) and final-low-rising (SC high-level + low-rising) pseudo-words.

Figure 8

Figure 5. Upper panels: estimated smoothes (left) and the corresponding difference curve (right) for JM final-rising words (JM low-falling + rising) identified as SC final-high-rising (SC falling + high-rising) and final-low-rising (SC falling + low-rising) pseudo-words. Lower panels: estimated smoothes (left) and the corresponding difference curve (right) for SC final-high-rising (SC low-falling + high-rising) and final-low-rising (SC falling + low-rising) pseudo-words.

Figure 9

Figure 6. Procedure for Experiment 3. “非词” corresponds to “nonword” and “是词” corresponds to “word”.

Figure 10

Figure 7. Responses to the primes split with the Tonal Condition of the Prime (SC high-rising and SC low-rising). Left panel: Prime acceptance rates (0-1) with standard errors; right panel: mean (estimated) lexical decision times (RT in natural logarithm, ms) with confidence intervals for the primes.

Figure 11

Figure 8. Responses to the JM real words targets semantically related to the JM words hinted by the SC pseudo-words, split with the Tonal Condition of the Prime (colour) and the Semantic Relatedness between the prime and the target (cluster), and whether the preceding SC prime was accepted as a JM real word (subplot). Left panels: target acceptance rates (0-1) with standard errors; right panels: mean (estimated) lexical decision times (RT in natural logarithm, ms) with confidence intervals for the primes.

Figure 12

Appendix. Crucial Stimuli

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