Child readers' eye movements in reading Thai

Training Children to Perceive Non-native Lexical Tones: Tone Language Background, Bilingualism, and Auditory-Visual Information

This study investigates the role of language background and bilingual status in the perception of foreign lexical tones. Eight groups of participants, consisting of children of 6 and 8 years from one of four language background (tone or non-tone) × bilingual status (monolingual or bilingual)-Thai monolingual, English monolingual, English-Thai bilingual, and English-Arabic bilingual were trained to perceive the four Mandarin lexical tones. Half the children in each of these eight groups were given auditory-only (AO) training and half auditory-visual (AV) training. In each group Mandarin tone identification was tested before and after (pre- and post-) training with both auditory-only test (ao-test) and auditory-visual test (av test). The effect of training on Mandarin tone identification was minimal for 6-year-olds. On the other hand, 8-year-olds, particularly those with tone language experience showed greater pre- to post-training improvement, and this was best indexed by ao-test trials. Bilingual vs. monolingual background did not facilitate overall improvement due to training, but it did modulate the efficacy of the Training mode: for bilinguals both AO and AV training, and especially AO, resulted in performance gain; but for monolinguals training was most effective with AV stimuli. Again this effect was best indexed by ao-test trials. These results suggest that tone language experience, be it monolingual or bilingual, is a strong predictor of learning unfamiliar tones; that monolinguals learn best from AV training trials and bilinguals from AO training trials; and that there is no metalinguistic advantage due to bilingualism in learning to perceive lexical tones.

Application of Linear Mixed-Effects Models in Human Neuroscience Research: A Comparison with Pearson Correlation in Two Auditory Electrophysiology Studies

Neurophysiological studies are often designed to examine relationships between measures from different testing conditions, time points, or analysis techniques within the same group of participants. Appropriate statistical techniques that can take into account repeated measures and multivariate predictor variables are integral and essential to successful data analysis and interpretation. This work implements and compares conventional Pearson correlations and linear mixed-effects (LME) regression models using data from two recently published auditory electrophysiology studies. For the specific research questions in both studies, the Pearson correlation test is inappropriate for determining strengths between the behavioral responses for speech-in-noise recognition and the multiple neurophysiological measures as the neural responses across listening conditions were simply treated as independent measures. In contrast, the LME models allow a systematic approach to incorporate both fixed-effect and random-effect terms to deal with the categorical grouping factor of listening conditions, between-subject baseline differences in the multiple measures, and the correlational structure among the predictor variables. Together, the comparative data demonstrate the advantages as well as the necessity to apply mixed-effects models to properly account for the built-in relationships among the multiple predictor variables, which has important implications for proper statistical modeling and interpretation of human behavior in terms of neural correlates and biomarkers.