Seeking the neural traces of statistical learning during implicit processing of visual words

Abstract

Abstract Statistical learning (SL) plays a key role in literacy acquisition. Studies have increasingly revealed the influence of SL on visual word processing, including the effects of word frequency at the lexical level and mappings between orthography, phonology, and semantics at the sub-lexical level. However, there has been scant direct evidence supporting neural representations of statistical regularities in visual word processing. Using time-resolved representational similarity analysis (RSA), the present study examined neural representations of different types of statistical regularities in visual word processing. From the perspective of predictive coding, an equal probability sequence with low built-in prediction precision and three oddball sequences with high built-in prediction precision were designed with consistent and three types of inconsistent (orthographically inconsistent, orthography-to-phonology inconsistent, and orthography-to-semantics inconsistent) Chinese characters as visual stimuli. In the three oddball sequences, consistent characters were set as the standard stimuli (probability of occurrence p = 0.75) and three types of inconsistent characters were set as deviant stimuli (p = 0.25), respectively. In the equal probability sequence, the same consistent and inconsistent characters were presented randomly with identical occurrence probability (p = 0.25). Significant neural representation activities of character frequency were observed in the equal probability sequence. By contrast, neural representations of sub-lexical statistics only emerged in oddball sequences where short-term predictions were shaped. These findings reveal that the statistical information obtained through long-term SL continues to play a role in current word processing mechanisms and these mechanisms can be modulated by short-term predictions.