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Title
Eyes on words : a fixation-related fMRI study of the left occipitotemporal cortex during selfpaced silent reading of words and pseudowords / Sarah Schuster, Stefan Hawelka, Fabio Richlan, Philipp Ludersdorfer & Florian Hutzler
AuthorSchuster, Sarah ; Hawelka, Stefan ; Richlan, Fabio ; Ludersdorfer, Philipp ; Hutzler, Florian In der Gemeinsamen Normdatei der DNB nachschlagen
Published in
Scientific Reports, London, 2015, Vol. 5, Issue Article number 12686, page 1-11
PublishedNature Publishing Group, 2015
DescriptionDiagramme
LanguageEnglish
Document typeJournal Article
Keywords (EN)Human behaviour / Reading
Project-/ReportnumberP 25799
ISSN2045-2322
URNurn:nbn:at:at-ubs:3-2785 Persistent Identifier (URN)
DOI10.1038/srep12686 
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 The work is publicly available
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Eyes on words [0.89 mb]
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Abstract (English)

The predominant finding of studies assessing the response of the left ventral occipito-temporal cortex (vOT) to familiar words and to unfamiliar, but pronounceable letter strings (pseudowords) is higher activation for pseudowords. One explanation for this finding is that readers automatically generate predictions about a letter strings identity pseudowords mismatch these predictions and the higher vOT activation is interpreted as reflecting the resultant prediction errors. The majority of studies, however, administered tasks which imposed demands above and beyond the intrinsic requirements of visual word recognition. The present study assessed the response of the left vOT to words and pseudowords by using the onset of the first fixation on a stimulus as time point for modeling the BOLD signal (fixation-related fMRI). This method allowed us to assess the neural correlates of self-paced silent reading with minimal task demands and natural exposure durations. In contrast to the predominantly reported higher vOT activation for pseudowords, we found higher activation for words. This finding is at odds with the expectation of higher vOT activation for pseudowords due to automatically generated predictions and the accompanying elevation of prediction errors. Our finding conforms to an alternative explanation which considers such top-down processing to be non-automatic and task-dependent.

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CC-BY-License (4.0)Creative Commons Attribution 4.0 International License