ERPs reveal sensitivity to hypothetical contexts in spoken discourse
Bibliographic record
Abstract
We used event-related potentials to examine the interaction between two dimensions of discourse comprehension: (i) referential dependencies across sentences (e.g. between the pronoun 'it' and its antecedent 'a novel' in: 'John is reading a novel. It ends quite abruptly'), and (ii) the distinction between reference to events/situations and entities/individuals in the real/actual world versus in hypothetical possible worlds. Cross-sentential referential dependencies are disrupted when the antecedent for a pronoun is embedded in a sentence introducing hypothetical entities (e.g. 'John is considering writing a novel. It ends quite abruptly'). An earlier event-related potential reading study showed such disruptions yielded a P600-like frontal positivity. Here we replicate this effect using auditorily presented sentences and discuss the implications for our understanding of discourse-level language processing.
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How this classification was reachedexpand
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 | 0.003 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".