Temporal gradient characteristics of automatic neural representation for orthographic consistency in Chinese characters

Abstract

AbstractVisual word processing involves the automatic decoding of orthographic, phonological and semantic information. The mappings of these information in a writing system comprise an important feature of visual words: orthographic regularity and consistency. Recent electroencephalography (EEG) studies have investigated the automatic processing mechanism of orthographic regularity (i.e., words vs. pseudowords) among visual words. However, the automated mechanism and temporal dynamics of different types of orthographic consistency effects (e.g., orthographic positional consistency, orthography-to-phonology consistency vs. orthography-to-semantics consistency) have never been studied. This study explored automatic neural representation for orthographic consistency effects in visual words and its temporal dynamics through an oddball paradigm. Three types of oddball sequences were designed with Chinese characters as stimuli, including consistent Chinese characters as standard stimuli and three types of inconsistent characters (orthographic positional inconsistent, orthography-to-phonology inconsistent vs. orthography-to-semantics inconsistent) as deviant stimuli, respectively. Significant visual mismatch negativity (vMMN) activities were observed in all three types of inconsistent characters, which suggests automatic processing caused by orthographic consistency violations. Time-resolved representational similarity analysis (RSA) further revealed that there are different temporal dynamics of automatic neural representations for the three types of consistency features. The representation of positional consistency emerged earlier within an independent time window, while the representation of phonetic and semantic consistency emerged later, and partially overlapped. These findings provide novel insights for the temporal gradient characteristics of automated representation structure of orthography consistency information.