Seeking the neural representation of statistical properties in print during implicit processing of visual words

Abstract

Statistical learning (SL) plays a key role in literacy acquisition. Studies have increasingly revealed the influence of distributional statistical properties of words on visual word processing, including the effects of word frequency (lexical level) and mappings between orthography, phonology, and semantics (sub-lexical level). However, there has been scant evidence to directly confirm that the statistical properties contained in print can be directly characterized by neural activities. Using time-resolved representational similarity analysis (RSA), the present study examined neural representations of different types of statistical properties 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 word 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 properties learned from long-term print environment continues to play a role in current word processing mechanisms and these mechanisms can be modulated by short-term predictions.

Fig. 1. RSA results.

Time course of partial Spearman correlations between EEG RDMs and predictor RDMs for orthographic (red), phonological (yellow), semantic (blue), and frequency (green) in equal-probability sequences (a), in oddball sequences (b), and the difference between them (oddball minus the equal-probability) (c). Time course of partial Spearman correlations between EEG RDMs and rating RDMs for orthographic (red), phonological (yellow), semantic (blue), and frequency (green) in equal-probability sequences (d), in oddball sequences (e), and the difference between them (f).

Fig. 2. Spatiotemporal distribution of vMMN activities.

vMMN waveforms that obtained by subtracting the standard from the deviant characters of each consistency dimension at left (a) and right (b) ROIs. Scalp topographic maps of vMMNs in two active time periods (150–300 ms (c) and 310–500 ms (d)).

Fig. 3. Illustration of the experimental procedure.

a Details of the selected Chinese characters. b Examples of presentation settings for consistent and inconsistent characters in different blocks. c Schematic depiction of the color-change judgment task in the equal probability block. d Schematic depiction of the color-change judgment task in the oddball block. Abbreviations: CC consistent characters, IOr inconsistent orthographic characters, IPh inconsistent phonological characters, ISe inconsistent semantic characters.

Fig. 4. Schematic for representational similarity analyses of EEG data.

a Neural RDMs are constructed for each data point by comparing pairwise character-specific activations. RDMs are symmetric with a diagonal of zeros, and their size corresponds to the number of inconsistent characters, here 9 × 9. b Model RDMs for different dimensional statistical information. Finally, the partial correlation coefficients between neural RDMs and model RDMs was calculated for each subject at each time point to quantify the neural representation strength.