ERP Correlates of Letter Identity and Letter Position Are Modulated by Lexical Frequency

Autor/inn/en	Vergara-Martinez, Marta; Perea, Manuel; Gomez, Pablo; Swaab, Tamara Y.
Titel	ERP Correlates of Letter Identity and Letter Position Are Modulated by Lexical Frequency
Quelle	In: Brain and Language, 125 (2013) 1, S.11-27 (17 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0093-934X
DOI	10.1016/j.bandl.2012.12.009
Schlagwörter	Semantics; Word Recognition; Stimuli; Diagnostic Tests; Brain Hemisphere Functions; Correlation; Models; Classification; Alphabets; Word Frequency; Decision Making; Computational Linguistics + Suchen Sie Ihr Suchwort? Semantik; Worterkennung; Anreizsystem; Diagnostic test; Diagnostischer Test; Korrelation; Analogiemodell; Classification system; Klassifikation; Klassifikationssystem; Buchstabenschrift; Word analysis; Frequency; Wortanalyse; Häufigkeit; Decision-making; Entscheidungsfindung; Linguistics; Computerlinguistik
Abstract	The encoding of letter position is a key aspect in all recently proposed models of visual-word recognition. We analyzed the impact of lexical frequency on letter position assignment by examining the temporal dynamics of lexical activation induced by pseudowords extracted from words of different frequencies. For each word (e.g., BRIDGE), we created two pseudowords: A transposed-letter (TL: BRIGDE) and a replaced-letter pseudoword (RL: BRITGE). ERPs were recorded while participants read words and pseudowords in two tasks: Semantic categorization (Experiment 1) and lexical decision (Experiment 2). For high-frequency stimuli, similar ERPs were obtained for words and TL-pseudowords, but the N400 component to words was reduced relative to RL-pseudowords, indicating less lexical/semantic activation. In contrast, TL- and RL-pseudowords created from low-frequency stimuli elicited similar ERPs. Behavioral responses in the lexical decision task paralleled this asymmetry. The present findings impose constraints on computational and neural models of visual-word recognition. (Contains 4 tables and 9 figures.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10