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The time course of visual word recognition as revealed by linear regression analysis of ERP data

The time course of visual word recognition as revealed by linear regression analysis of ERP data

Neuroimage 30(4): 1383-1400

EEG correlates of a range of psycholinguistic word properties were used to investigate the time course of access to psycholinguistic information during visual word recognition. Neurophysiological responses recorded in a visual lexical decision task were submitted to linear regression analysis. First, 10 psycholinguistic features of each of 300 stimulus words were submitted to a principal component analysis, which yielded four orthogonal variables likely to reflect separable processes in visual word recognition: Word length, Letter n-gram frequency, Lexical frequency and Semantic coherence of a word's morphological family. Since the lexical decision task required subjects to distinguish between words and pseudowords, the binary variable Lexicality was also investigated using a factorial design. Word-pseudoword differences in the event-related potential first appeared at 160 ms after word onset. However, regression analysis of EEG data documented a much earlier effect of both Word length and Letter n-gram frequency around 90 ms. Lexical frequency showed its earliest effect slightly later, at 110 ms, and Semantic coherence significantly correlated with neurophysiological measures around 160 ms, simultaneously with the lexicality effect. Source estimates indicated parieto-temporo-occipital generators for the factors Length, Letter n-gram frequency and Word frequency, but widespread activation with foci in left anterior temporal lobe and inferior frontal cortex related to Semantic coherence. At later stages (>200 ms), all variables exhibited simultaneous EEG correlates. These results indicate that information about surface form and meaning of a lexical item is first accessed at different times in different brain systems and then processed simultaneously, thus supporting cascaded interactive processing models.

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Accession: 012684698

Download citation: RISBibTeXText

PMID: 16460964

DOI: 10.1016/j.neuroimage.2005.11.048

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