bioRxivNeurosciencePreprint
Early detection of sublexical and lexical processing in beginning readers: evidence from Steady-State Visual Evoked Potentials (SSVEPs)
Fang Wang, Quynh Trang H. Nguyen, Blair KaneshiroiD, Lindsey R. HasakiD, Angie M. WangiD, Elizabeth Y. ToomarianiD, Anthony M. NorciaiD, Bruce D. McCandlissiD
Abstract
There are multiple levels of processing relevant to reading that vary in their visual, sublexical and lexical orthographic processing demands. Segregating distinct cortical sources for each of these levels has been challenging in EEG studies of early readers. To address this challenge, we applied recent advances in analyzing high-density EEG using Steady-State Visual Evoked Potentials (SSVEPs) via data-driven Reliable Components Analysis (RCA) in a group of early readers spanning from kindergarten to second grade. Three controlled stimulus contrasts--familiar words versus unfamiliar pseudofonts, familiar words versus orthographically legal pseudowords, and orthographically legal pseudowords versus orthographically illegal nonwords--were used to isolate visual print/letter selectivity, sublexical processing, and lexical processing, respectively. We found robust responses specific to each of these processing levels, even in kindergarteners who have limited knowledge of print. Moreover, comparing amplitudes of these three stimulus contrasts across three reading fluency-based groups and three grade-based groups revealed fluency group and grade group main effects only for lexical contrast (i.e., words versus orthographically legal pseudowords). Furthermore, we found that sublexical orthography-related responses shifted their topographic distribution from the right to left hemisphere from kindergarten to first and second grades. Results suggest that, with more sensitive measures, the sublexical and lexical fine tuning for words--as a bio-marker of reading ability--can be detected at a much earlier stage than previously assumed. Declarations of interestNone
- organism: human
- assay: electronic health records
- research focus: cell type annotation
- approach: statistical model
- Citations
- 0
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