Poster C64, Sunday, March 26, 5:00 – 7:00 pm, Pacific Concourse
Predicting tonal language learning aptitude from individual differences in brain morphology and microstructure
Dimitrios Donavos1, Anita Bowles1,2; 1University of Maryland Center for Advanced Study of Language, 2Rosetta Stone, Ltd.
For adult second language (L2) learners of tonal languages such as Mandarin Chinese, which use pitch to signal lexical contrasts, accurately perceiving and producing lexical tone can present a significant challenge. To help identify learners who are most likely to succeed and learners who might benefit from particular types of training, Bowles et al. (2015) examined potential components of aptitude for mastering L2 lexical tone. Across five laboratory sessions, 160 native English speakers with no previous tone language experience received training on Mandarin words and completed tests of pitch ability and musicality, as well as measures of general cognitive ability and L2 aptitude. Pitch ability was the strongest predictor of word learning, and both pitch ability and musicality improved predictions beyond measures of general cognitive ability and L2 aptitude. These findings suggest that pitch-specific perceptual measures are critical components of measuring aptitude for tonal language learning. The present study involves a subset of 75 participants from the larger behavioral study who received high resolution structural (MPRAGE) and diffusion tensor imaging (DTI) functional magnetic resonance imaging (fMRI) scans prior to beginning the word-learning task. We investigated whether individual differences in morphology and microstructure of grey and white matter, specifically right hemisphere posterior parietal regions implicated in tonal and visuo-spatial learning of Mandarin (Qi et al., 2015), could predict Mandarin word-learning performance and whether that predictive power exceeded what was achieved with behavioral measures alone. Implications for the relationship between morphological structure and successful tonal language learning will be discussed.
Topic Area: LANGUAGE: Other