The Neural Signature of Statistical Learning of Orthography.

Xiuhong Tong,Yi Wang,Shelley Xiuli Tong

doi:10.3389/fnhum.2020.00026

Abstract

While an increasing number of behavioral studies suggest the importance of statistical learning in acquiring orthographic regularity across writing systems, no direct neural evidence supports this claim. The present study used event-related potentials (ERPs) to investigate the time course and the neural correlate of statistical learning of positional consistency in Chinese orthography. Visual ERPs were recorded, while Chinese adults performed an orthographic statistical learning task involving artificial characters varying in high, moderate, and low levels of positional consistency. The negative ERP deflection at the N1 time window, typically linked with orthographic regularity processing, was found in orthographic statistical learning with the low and moderate consistencies eliciting larger neural responses than the high consistency in the time window of 150–210 ms over occipital–temporal brain areas. These results suggest that orthographic statistical learning begins within the first 210 ms and that the N1 might be its neural indicator.

Highlights

An increasing number of recent studies show that statistical learning is useful for spoken language acquisition and plays a role in orthographic learning, which is the process of acquiring word-specific orthographic representations essential for reading and writing
Follow-up contrasts demonstrated that participants were more accurate in the high-consistency condition than the moderateand low-consistency conditions. It suggests that the participants were sensitive to the positional consistency of target radicals, and the high consistency facilitated their recognition of pseudocharacters
Our core finding is that the low- and moderateconsistency levels elicited a larger neural response in the time window of 150–210 ms than the high-consistency level over the occipital–temporal area of the brain

Summary

Introduction

An increasing number of recent studies show that statistical learning is useful for spoken language acquisition (for a review, see Erickson and Thiessen, 2015) and plays a role in orthographic learning, which is the process of acquiring word-specific orthographic representations essential for reading and writing (for a review, see Castles et al, 2018). Statistical learning, or the ability to extract and integrate statistical properties of environmental input, such as frequency and variability, has been shown to be a powerful tool that helps Chinese children learn a large number of visually complex characters in the process of becoming literate (e.g., Arciuli and Simpson, 2011; Yin and McBride, 2015; He and Tong, 2017). Some radicals only appear in a specific location when forming a Chinese character.

Methods

Results

Conclusion