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Record W2085721366 · doi:10.1162/jocn.2009.21286

Encoding in the Visual Word Form Area: An fMRI Adaptation Study of Words versus Handwriting

2009· article· en· W2085721366 on OpenAlexafffund
Jason J.S. Barton, Christopher J. Fox, Alla Sekunova, Giuseppe Iaria

Bibliographic record

VenueJournal of Cognitive Neuroscience · 2009
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsHandwritingPsychologyFusiform gyrusFusiform face areaCognitive psychologyAdaptation (eye)Reading (process)Word recognitionPerceptionLinguisticsNeuroscienceCognitionFace perception

Abstract

fetched live from OpenAlex

Written texts are not just words but complex multidimensional stimuli, including aspects such as case, font, and handwriting style, for example. Neuropsychological reports suggest that left fusiform lesions can impair the reading of text for word (lexical) content, being associated with alexia, whereas right-sided lesions may impair handwriting recognition. We used fMRI adaptation in 13 healthy participants to determine if repetition-suppression occurred for words but not handwriting in the left visual word form area (VWFA) and the reverse in the right fusiform gyrus. Contrary to these expectations, we found adaptation for handwriting but not for words in both the left VWFA and the right VWFA homologue. A trend to adaptation for words but not handwriting was seen only in the left middle temporal gyrus. An analysis of anterior and posterior subdivisions of the left VWFA also failed to show any adaptation for words. We conclude that the right and the left fusiform gyri show similar patterns of adaptation for handwriting, consistent with a predominantly perceptual contribution to text processing.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.103
GPT teacher head0.375
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations50
Published2009
Admission routes2
Has abstractyes

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