The visual word form area and the frequency with which words are encountered: evidence from a parametric fMRI study
Abstract
Cohen and Dehaene et al. proposed that the Visual Word Form Area (VWFA) in the left midfusiform gyrus, contrary to its name, is limited to the extraction of an abstract letter string and not involved in proper visual word recognition. We examined this prelexical function of the VWFA by a parametric block design with five levels of written word frequency. The lowest level was represented by pseudowords and the highest level by words of very high frequency. Contrary to the assumed prelexical function of the VWFA, increasing frequency was associated with decreasing brain activation in a large posterior cluster of the left hemisphere including middle and posterior fusiform regions. The same negative relation between frequency and activation was found in several left frontal clusters. The relation of increasing frequency and decreasing activation in occipitotemporal regions corresponds to a similar relation in the same brain regions found by studies which experimentally manipulated object or face familiarity. This convergence suggests that fusiform regions are specialized for extracting and storing abstract patterns when processing visual objects and these patterns serve as recognition units in subsequent encounters with the same objects.
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The left occipitotemporal cortex is specialised for extracting and storing abstract whole word patterns.
“To explain these “lexical familiarity effects”, Kronbichler et al. (2004) proposed that the left occipitotemporal cortex was specialised for extracting and storing abstract whole word patterns.”
From A review and synthesis of the first 20years of PET and fMRI studies of heard speech, spoken language and reading (2012) · cites this paper as Kronbichler et al., 2004
Left occipitotemporal activation was less for words with high relative to low lexical frequency.
“However, this perspective did not explain why left occipitotemporal activation was less for (a) words with high relative to low lexical frequency (Kuo et al., 2003; Kronbichler et al., 2004)”
From A review and synthesis of the first 20years of PET and fMRI studies of heard speech, spoken language and reading (2012) · cites this paper as Kronbichler et al., 2004
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- Authors
- Kronbichler, M., Hutzler, F., Wimmer, H., Mair, A., Staffen, W., & Ladurner, G.
- Affiliations
- University of Salzburg (AT)
- Christian Doppler Klinik (AT)
- Corresponding author
- Kronbichler, Martin
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2004-03
- Volume
- 21
- Issue
- 3
- Pages
- 946-953
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2076114001
- Semantic Scholar
- c46b65e29113c9b6d1a8fe555233b34fa594ac99
- Cited by
- 329
- References
- 31
- Citations by year
- 2026: 12 · 2025: 11 · 2024: 12 · 2023: 10 · 2022: 11 · 2021: 13 · 2020: 9 · 2019: 19 · 2018: 13 · 2017: 4 · 2016: 13 · 2015: 23 · 2014: 14 · 2013: 22 · 2012: 23
- FWCI
- 23.74
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- 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 › Visual perception and processing mechanisms
- Keywords
- visual word form area, word frequency, fusiform gyrus, visual word recognition, prelexical processing, pattern recognition, object familiarity, face familiarity, left hemisphere, occipitotemporal cortex, pseudowords
- MeSH terms
- Adolescent, Adult, Female, Fixation, Ocular, Humans, Image Interpretation, Computer-Assisted, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Photic Stimulation, Psychomotor Performance, Reading, Databases, Factual
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.
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