An Electrophysiological Study of Noisy Channel Sentence Comprehension.
Bibliographic record
Abstract
The present study employed Event Related Potential (ERP) paradigm to investigate on-line sentence comprehension containing deletion and insertion errors, compared to controls. We used prepositional-object (PO) plausible constructions such as (i) The aunt mailed the letter to her niece by post, compared to double-object (DO) implausible constructions (ii) # The aunt mailed the letter_her niece by post (deletion error). Besides, a DO plausible construction such as (iii) The aunt mailed her niece the letter by post, was compared to a PO implausible construction (iv) The aunt mailed her niece #to the letter by post (insertion error). Based on noisy Channel model propositions, language processing system acts as a rational comprehender and follows Bayesian size principle, which posits deletions are more likely to occur than insertions. This assumption would be held true if, comprehenders show differential sensitivity to different error types. Behavioural and ERP data from our study revealed the same. The brain responded with a sustained negativity (anterior) at head-noun position for deletion errors, while insertion errors elicited a long-lasting centro-parietal positivity at a later sentence position. The findings indicate that the language processing system is immediate in detecting anomaly and might engaged in a lexical search process (depicting higher workload) for deletion errors. The positivity observed for insertion error was thought to be associated with a repair process because of a clear availability of alternative plausible meaning due to semantic attraction. Together, these findings support the noisy channel model assumption for differential treatment of deletion versus insertion errors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".