Processes of Discourse Integration: Evidence From Event-Related Brain Potentials
Bibliographic record
Abstract
We used ERP methodology to investigate how readers validate discourse concepts and update situation models when those concepts followed factive (e.g., knew) and nonfactive (e.g., guessed) verbs, and also when they were true, false, or indeterminate with reference to previous discourse. Following factive verbs, early (P2) and later brain components (N400 and late frontal positivity) revealed that relative to true concepts, both false and indeterminate concepts were more difficult to validate, and only indeterminate concepts were ultimately updated into the situation model. Following nonfactive verbs, there was no evidence of situational model updating for any condition. However, there was a clear N400 gradient that suggests the lower commitment of nonfactive verbs leads to less incongruence with discourse context for the indeterminate condition than the false condition. These results provide novel insight into how pragmatic constraints afforded by verbs influence discourse validation and the updating of situation models.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".