Learning to Perceive Non-Native Tones via Distributional Training: Effects of Task and Acoustic Cue Weighting

Abstract

As many distributional learning (DL) studies have shown, adult listeners can achieve discrimination of a difficult non-native contrast after a short repetitive exposure to tokens falling at the extremes of that contrast. Such studies have shown using behavioural methods that a short distributional training can induce perceptual learning of vowel and consonant contrasts. However, much less is known about the neurological correlates of DL, and few studies have examined non-native lexical tone contrasts. Here, Australian-English speakers underwent DL training on a Mandarin tone contrast using behavioural (discrimination, identification) and neural (oddball-EEG) tasks, with listeners hearing either a bimodal or a unimodal distribution. Behavioural results show that listeners learned to discriminate tones after both unimodal and bimodal training; while EEG responses revealed more learning for listeners exposed to the bimodal distribution. Thus, perceptual learning through exposure to brief sound distributions (a) extends to non-native tonal contrasts, and (b) is sensitive to task, phonetic distance, and acoustic cue-weighting. Our findings have implications for models of how auditory and phonetic constraints influence speech learning.

Keywords: acoustic cue-weighting; discrimination; distributional learning; identification; oddball-EEG; phonetic distance; tone.

Figure 1

Pitch contours along a /taT1/–/taT4/ continuum (Stimuli and figure from [28]).

Figure 2

Frequency of occurrence for each training token encountered by listeners in the unimodal (grey line) and bimodal (black line) conditions. Figure from [55].

Figure 3

Mean accuracy percentage before and after distributional learning (Error bars = ±1 standard error). The horizontal line indicates chance level (50%) performance.

Figure 4

Mean percentage of “falling” classifications for steps 3 and 6 before and after bimodal (left) and unimodal (right) distributional learning (Error bars = ±1 standard error) White bars indicate performance at pretest, and black bars posttest. The horizontal line indicates chance (50%) performance.

Figure 5

Grand-averaged MMN component by unimodal (left) and bimodal (right) condition. Dotted lines show the MMN component at pre-training and solid lines represent the MMN component at post-training. The red boxes highlight the time window in which the MMN amplitude peaks were measured (i.e., 120–270 ms post-stimulus onset to account for consonant production).

Figure 6

Mean MMN amplitude (large dots) for the two conditions at each test phase. Small dots represent individual data. Error bars represent one standard error. Asterisks represent significant MMN amplitude.