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Japanese co-occurrence restrictions influence second language perception

Published online by Cambridge University Press:  30 January 2019

ALEXANDER J. KILPATRICK*
Affiliation:
University of Melbourne
RIKKE L. BUNDGAARD-NIELSEN
Affiliation:
MARCS Institute for Brain Behaviour and Development, Western Sydney University
BRETT J. BAKER
Affiliation:
University of Melbourne
*
ADDRESS FOR CORRESPONDENCE Alexander Kilpatrick, University of Melbourne, Babel Building University of Melbourne Parkville, Victoria 3052, Australia. E-mail: alex.kilpatrick@unimelb.edu.au
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Abstract

Most current models of nonnative speech perception (e.g., extended perceptual assimilation model, PAM-L2, Best & Tyler, 2007; speech learning model, Flege, 1995; native language magnet model, Kuhl, 1993) base their predictions on the native/nonnative status of individual phonetic/phonological segments. This paper demonstrates that the phonotactic properties of Japanese influence the perception of natively contrasting consonants and suggests that phonotactic influence must be formally incorporated in these models. We first propose that by extending the perceptual categories outlined in PAM-L2 to incorporate sequences of sounds, we can account for the effects of differences in native and nonnative phonotactics on nonnative and cross-language segmental perception. In addition, we test predictions based on such an extension in two perceptual experiments. In Experiment 1, Japanese listeners categorized and rated vowel–consonant–vowel strings in combinations that either obeyed or violated Japanese phonotactics. The participants categorized phonotactically illegal strings to the perceptually nearest (legal) categories. In Experiment 2, participants discriminated the same strings in AXB discrimination tests. Our results show that Japanese listeners are more accurate and have faster response times when discriminating between legal strings than between legal and illegal strings. These findings expose serious shortcomings in currently accepted nonnative perception models, which offer no framework for the influence of native language phonotactics.

Information

Type
Original Article
Copyright
© Cambridge University Press 2019 
Figure 0

Table 1 AXB test contrasts used in Experiment 2

Figure 1

Table 2 Selection of possible CV combinations in Conservative and Innovative Japanese

Figure 2

Table 3 Katakana graphemes used for transcribing CV combinations

Figure 3

Table 4 Categories presented in hiragana and romaaji

Figure 4

Table 5 Categorization patterns

Figure 5

Table 6 Within-category paired-sample t test results

Figure 6

Figure 1 Average goodness-of-fit results. Error bars indicate standard deviation.

Figure 7

Table 7 AXB discrimination trials organized into PAM-L2 based categories

Figure 8

Table 8 Goodness-of-fit ratings for prototypical and nonprototypical strings

Figure 9

Table 9 One-sample t tests measuring dissimilarity between AXB discrimination test results and chance results (50%)

Figure 10

Figure 2 Percent correct discrimination for the five contrasts. Error bars represent standard deviation.

Figure 11

Table 10 Bonferroni pairwise comparisons of AXB test results

Figure 12

Figure 3 Average response time for each AXB test. Error bars represent standard deviation.

Figure 13

Figure 4 Scatter plot showing the negatively correlated relationship between average response time and AXB test accuracy.

Figure 14

Table 11 Bonferroni pairwise comparisons of AXB test response time results