A predictive coding model for online sentence processing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Computational approaches to prediction in online sentence processing tend to be dominated by computation-level surprisal theory, offering few insights into underlying cognitive mechanisms. Conversely, predictive coding is an algorithmic theory grounded in neuroscience, but it has rarely been employed in the study of language processing, in part because its areas of application have not involved sequential processing. Building on a recently proposed temporal predictive coding model, we present what is to our knowledge the first exploration of sequential predictive coding in broad-coverage online sentence processing. We investigate our model at non-toy scale using naturally occurring language, establishing its cognitive validity via comparison with reading times, and we link measurable aspects of the model to cognitive discussions of mechanism for prediction in language processing. Our results suggest that sequential predictive coding models are a valuable complement to surprisal theory as a route to progress on process-oriented theories of language comprehension.
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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 | 0.000 | 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 | 0.000 | 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