Who are you talking to? The role of addressee identity in utterance comprehension
Bibliographic record
Abstract
Experimental evidence suggests that speaker and addressee quickly adapt to each other from the earliest moments of sentence processing, and that interlocutor-related information is rapidly integrated with other sources of nonpragmatic information (e.g., semantic, morphosyntactic, etc.). These findings have been taken as support for one-step models of sentence comprehension. The results from the present event-related potential study challenge this theoretical framework providing a case where discourse level information is integrated only at a late stage of processing, when morphosyntactic analysis has been already initiated. We considered the case of Basque allocutive agreement, where information about addressee gender is encoded in verbal inflection. Two different types of Basque grammatical violations were presented together with the corresponding control conditions: one could be detected based on a morphosyntactic mismatch (person agreement violation), while the other could be detected only if the addressee's gender was considered (allocutive violation). Morphosyntactic violations elicited greater N400 effects followed by P600 effects, while allocutive violations elicited only P600 effects. These results provide new constraints to one-step accounts as they represent a case where speakers do not immediately adjust to the addressee's perspective. We propose that the relevance of discourse-level information might be a crucial variable to reconcile the dichotomy between one- and two-step 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 | 0.001 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".