The Spatiotemporal Dynamics of Bottom–Up and Top–Down Processing during At-a-Glance Reading
Why this work is in the frame
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Bibliographic record
Abstract
Like all domains of cognition, language processing is affected by top-down knowledge. Classic evidence for this is missing blatant errors in the signal. In sentence comprehension, one instance is failing to notice word order errors, such as transposed words in the middle of a sentence: "you that read wrong" (Mirault et al., 2018). Our brains seem to fix such errors, since they are incompatible with our grammatical knowledge, but how do our brains do this? Following behavioral work on inner transpositions, we flashed four-word sentences for 300 ms using rapid parallel visual presentation (Snell and Grainger, 2017). We compared magnetoencephalography responses to fully grammatical and reversed sentences (24 human participants: 21 females, 4 males). The left lateral language cortex robustly distinguished grammatical and reversed sentences starting at 213 ms. Thus, the influence of grammatical knowledge begun rapidly after visual word form recognition (Tarkiainen et al., 1999). At the earliest stage of this neural "sentence superiority effect," inner transpositions patterned between grammatical and reversed sentences, showing evidence that the brain initially "noticed" the error. However, 100 ms later, inner transpositions became indistinguishable from grammatical sentences, suggesting at this point, the brain had "fixed" the error. These results show that after a single glance at a sentence, syntax impacts our neural activity almost as quickly as higher-level object recognition is assumed to take place (Cichy et al., 2014). The earliest stage involves detailed comparisons between the bottom-up input and grammatical knowledge, while shortly afterward, top-down knowledge can override an error in the stimulus.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it