Hemispheric involvement in the processing of Chinese idioms: An fMRI study

Jie Yang, Ping Li, Xiaoping Fang, Hua Shu, Youyi Liu*, Lang Chen

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    30 Citations (Scopus)

    Abstract

    Although the left hemisphere is believed to handle major language functions, the role of the right hemisphere in language comprehension remains controversial. Recently researchers have investigated hemispheric language processing with figurative language materials (e.g., metaphors, jokes, and idioms). The current study capitalizes on the pervasiveness and distinct features of Chinese idioms to examine the brain mechanism of figurative language processing. Native Chinese speakers performed a non-semantic task while reading opaque idioms, transparent idioms, and non-idiomatic literal phrases. Whole-brain analyses indicated strong activations for all three conditions in an overlapping brain network that includes the bilateral inferior/middle frontal gyrus and the temporo-parietal and occipital-temporal regions. The two idiom conditions elicited additional activations in the right superior parietal lobule and right precuneus. Item-based modulation analyses further demonstrated that activation amplitudes in the right angular gyrus, right superior parietal lobule and right precuneus, as well as left inferior temporo-occipital cortex, are negatively correlated with the semantic transparency of the idioms. These results suggest that both hemispheres are involved in idiom processing but they play different roles. Implications of the findings are discussed in light of theories of figurative language processing and hemispheric functions.

    Original languageEnglish
    Pages (from-to)12-24
    Number of pages13
    JournalNeuropsychologia
    Volume87
    DOIs
    Publication statusPublished - 1 Jul 2016

    Keywords

    • FMRI
    • Chinese
    • Idiom processing
    • Fine-coarse coding theory
    • Hemispheric language functions
    • Semantic transparency

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