Unique, Shared, and Dominant Brain Activation in Visual Word Form Area and Lateral Occipital Complex during Reading and Picture Naming
Bibliographic record
Abstract
Identifying printed words and pictures concurrently is ubiquitous in daily tasks, and so it is important to consider the extent to which reading words and naming pictures may share a cognitive-neurophysiological functional architecture. Two functional magnetic resonance imaging (fMRI) experiments examined whether reading along the left ventral occipitotemporal region (vOT; often referred to as a visual word form area, VWFA) has activation that is overlapping with referent pictures (i.e., both conditions significant and shared, or with one significantly more dominant) or unique (i.e., one condition significant, the other not), and whether picture naming along the right lateral occipital complex (LOC) has overlapping or unique activation relative to referent words. Experiment 1 used familiar regular and exception words (to force lexical reading) and their corresponding pictures in separate naming blocks, and showed dominant activation for pictures in the LOC, and shared activation in the VWFA for exception words and their corresponding pictures (regular words did not elicit significant VWFA activation). Experiment 2 controlled for visual complexity by superimposing the words and pictures and instructing participants to either name the word or the picture, and showed primarily shared activation in the VWFA and LOC regions for both word reading and picture naming, with some dominant activation for pictures in the LOC. Overall, these results highlight the importance of including exception words to force lexical reading when comparing to picture naming, and the significant shared activation in VWFA and LOC serves to challenge specialized models of reading or picture naming.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".