Lexical Representations in the Common and Specific Neural Networks for Visual, Phonological, and Semantic Processing in Chinese Reading

Previous studies have investigated the common and specific neural correlates underlying visuo-orthographic, phonological, and semantic processing in word reading. However, it remains unclear how those networks represent different types of lexical information and how such representations and the interactions between networks are modulated by task-induced processing demands. To address this issue, 32 native Chinese participants were scanned with fMRI while performing a localizer task, and two reading tasks designed to elicit high demands on visuo-orthographic processing (i.e., structural judgment task) and semantic processing (i.e., familiarity judgment task). Activation analyses identified both common and specific neural networks involved in visual, phonological, and semantic processing. Representational similarity analysis (RSA) further revealed that the common network represented multiple types of lexical information, whereas the specific networks selectively represented particular lexical information corresponding to their respective processing type. Moreover, processing demands modulated lexical representations of common and specific networks in distinct ways: the common network exhibited flexible representational patterns, representing task-relevant lexical information under high processing demands, whereas the specific networks showed process-dependent selectivity, representing corresponding lexical information only under high processing demands. Functional connectivity analyses further indicated that processing demands could modulate connectivity patterns among networks, particularly between the common and specific networks. These findings highlight the distinct functional roles of common and specific networks, providing a new perspective on the complementary contributions of functionally overlapping and specialized systems in word reading.