Reading ahead: Localized neural signatures of parafoveal word processing and skipping decisions
Bibliographic record
Abstract
ABSTRACT Visual reading proceeds fixation-by-fixation, with individual words recognized and integrated into evolving conceptual representations within only hundreds of milliseconds. This relies, in part, on interactions between cognitive and oculomotor systems, such that linguistic properties of words influence eye movements and fixation durations. When and where do these influences arise in the neural processing of an incoming word? To answer this, we combined magnetoencephalography (MEG) with eye-tracking in a natural story-reading paradigm. We replicated past findings that word frequency and predictability have additive influences on fixation durations. Next, we identified putative generators of these influences in localized brain activity time-locked to fixation onsets. Both properties independently influenced neural responses in left occipitotemporal and ventral temporal areas, at latencies early enough to influence subsequent saccade planning. These effects began in posterior areas (the left lingual gyrus, lateral occipital cortex) during parafoveal word processing, and shifted more anteriorly (the inferior temporal and parahippocampal gyri) when the word was fixated in foveal vision. Evidence for parallel processing of both parafoveal and foveal words was observed in the left posterior fusiform, which housed near-simultaneous effects of both the fixated and upcoming words’ frequency and surprisal. We also found that parafoveal processing in this region, together with the left middle temporal gyrus, distinguished whether an upcoming word was skipped or fixated. These results suggest that during natural visual reading, word recognition and integration begin parafoveally, underpinned by a left-lateralized occipitotemporal system, where word processing rapidly exerts downstream influences on eye movement decisions.
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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".