Representation of word parts and wholes in occipitotemporal cortex
Bibliographic record
Abstract
We used fMRI to study the cortical representation of words that were split in half between right and left visual hemifields (split at fixation). We measured fMRI responses to words that repeated or changed in one of four possible ways: (1) the whole word repeated; (2) the whole word changed; (3) the left (but not the right) half of the word changed; or (4) the right (but not the left) half of the word changed. We observed substantially decreased fMRI responses to repeated stimuli relative to words that changed (fMRI adaptation) in occipitotemporal cortex (OT), including the ‘visual word form area’ (VWFA) and the ‘occipital face area’ (OFA) in posterior OT. We observed maximal adaptation in the VWFA and bilateral OFA. The VWFA adapted to whole-word repetitions and the OFA adapted to both whole-word repetitions and contralateral half-word repetitions. The exclusive whole-word adaptation effect was unique to the VWFA in left OT; whole-word adaptation was not observed in a putative right OT homologue. Adaptation to contralateral half-word repetitions was maximal in the right OFA and observed to a lesser degree in left OFA. Early visual areas showed equivalent fMRI responses to half-word repetitions. A second experiment showed that, in addition to whole-word adaptation, only the VWFA in left OT showed stronger fMRI responses to four-letter words versus non-word strings of similar retinal size and visual complexity. We conclude that posterior portions of OT represent word parts and the VWFA in left OT represents words as whole units. The VWFA is thus functionally distinct from other more posterior portions of OT that also respond strongly to word stimuli, such as the OFA. Meeting abstract presented at VSS 2013
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".