Privileged versus shared knowledge about object identity in real-time referential processing
Bibliographic record
Abstract
A central claim in research on interactive conversation is that listeners use the knowledge assumed to be shared with a conversational partner to guide their understanding of utterances from the earliest moments of processing. In the present study we investigated whether this claim extends to cases where shared vs. private knowledge is discrepant in terms of the identity assigned to a mutually seen object that could be misidentified on the basis of its appearance. Eye movement measures were used to evaluate listeners' ability to integrate a speaker's perspective as they identified the referent for an unfolding expression. The results reconfirmed previous findings showing that listeners can rapidly take into account a speaker's awareness of the existence/presence of a referential object. In contrast, however, listeners showed strong consideration of their private knowledge about the identity of an object during referential processing. Strikingly, this tendency was found even when speaker-produced discourse reinforced the way in which the speaker's understanding of the object's identity differed from that of the listener. Together, the results reveal clear and important differences in the way in which distinct types of perspective-based cues are integrated in real-time communicative interaction.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".