Neural correlates of lexical access during visual word recognition
Abstract
People can discriminate real words from nonwords even when the latter are orthographically and phonologically word-like, presumably because words activate specific lexical and/or semantic information. We investigated the neural correlates of this identification process using event-related functional magnetic resonance imaging (fMRI). Participants performed a visual lexical decision task under conditions that encouraged specific word identification: Nonwords were matched to words on orthographic and phonologic characteristics, and accuracy was emphasized over speed. To identify neural responses associated with activation of nonsemantic lexical information, processing of words and nonwords with many lexical neighbors was contrasted with processing of items with no neighbors. The fMRI data showed robust differences in activation by words and word-like nonwords, with stronger word activation occurring in a distributed, left hemisphere network previously associated with semantic processing, and stronger nonword activation occurring in a posterior inferior frontal area previously associated with grapheme-to-phoneme mapping. Contrary to lexicon-based models of word recognition, there were no brain areas in which activation increased with neighborhood size. For words, activation in the left prefrontal, angular gyrus, and ventrolateral temporal areas was stronger for items without neighbors, probably because accurate responses to these items were more dependent on activation of semantic information. The results show neural correlates of access to specific word information. The absence of facilitatory lexical neighborhood effects on activation in these brain regions argues for an interpretation in terms of semantic access. Because subjects performed the same task throughout, the results are unlikely to be due to task-specific attentional, strategic, or expectancy effects.
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- Authors
- Binder, J. R., McKiernan, K. A., Parsons, M. E., Westbury, C. F., Possing, E. T., Kaufman, J. N., & Buchanan, L.
- Affiliations
- Medical College of Wisconsin (US)
- West Virginia University (US)
- University of Alberta (CA)
- University of Windsor (CA)
- Venue
- Journal of Cognitive Neuroscience
- Publisher
- MIT Press - Journals
- Published
- 2003-04-01
- Volume
- 15
- Issue
- 3
- Pages
- 372-393
- ISSN
- 0898-929X (ISSN-L)
- 1530-8898
- OpenAlex
- W2123480151
- Semantic Scholar
- 4f572f75520060f3553b1c574c4811ad4f11d735
- Cited by
- 344
- References
- 101
- Citations by year
- 2026: 8 · 2025: 8 · 2024: 5 · 2023: 6 · 2022: 5 · 2021: 16 · 2020: 11 · 2019: 29 · 2018: 12 · 2017: 11 · 2016: 14 · 2015: 14 · 2014: 19 · 2013: 11 · 2012: 22
- FWCI
- 30.67
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Cognitive Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute of Mental Health (P01MH51358, P01MH051358)
- National Institute of Neurological Disorders and Stroke (R01NS033576, R01 NS33576)
- National Center for Research Resources (M01-RR00058, M01RR000058)
- Topics
- Social Sciences › Psychology › Developmental and Educational Psychology › Reading and Literacy Development
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neurobiology of Language and Bilingualism
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory Processes and Influences
- Keywords
- lexical access, visual word recognition, semantic access, inferior frontal cortex, left prefrontal cortex, angular gyrus, orthographic similarity, phonological similarity
- MeSH terms
- Adult, Brain, Brain Mapping, Female, Humans, Language, Magnetic Resonance Imaging, Male, Middle Aged, Reading, Semantics, Visual Perception, Vocabulary
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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