Neural Representation of Chinese Word Classes: An ALE Meta-Analysis of Noun and Verb Processing

Whether nouns and verbs rely on distinct neural systems remains debated, particularly in Chinese, which has limited inflectional morphology and flexible word-class use. We used activation likelihood estimation in GingerALE 3.0.2 to synthesize task-based fMRI/PET studies identified from three databases. Twenty-three publications contributed 28 experiment-level contrast entries. The primary analyses compared 8 noun-versus-baseline and 15 verb-versus-baseline entries, using a pooled 23-entry reference; the original mixed inputs of 9 noun-associated and 19 verb-associated entries were retained as additional analyses. Input maps used cluster-level family-wise error inference at p = 0.05, and final between-set contrasts used p = 0.05 with a 200 mm3 minimum cluster size. Noun-related convergence was concentrated in the left parietal region, whereas verb-related convergence involved frontal and posterior regions. The primary comparison identified greater noun-related convergence in the left parietal and right claustral/insular regions and greater verb-related convergence in bilateral frontal and right posterior regions. The baseline-only conjunction yielded no suprathreshold cluster, while the additional analyses showed a broadly comparable pattern. Together, these findings support partly differentiated neural processing of nouns and verbs in Chinese.