Adjustment of speaker’s referential expressions to an addressee’s likely knowledge and link with theory of mind abilities
Bibliographic record
Abstract
To communicate cooperatively, speakers must determine what constitutes the common ground with their addressee and adapt their referential choices accordingly. Assessing another person's knowledge requires a social cognition ability termed theory of mind (ToM). This study relies on a novel referential communication task requiring probabilistic inferences of the knowledge already held by an addressee prior to the study. Forty participants were asked to present 10 movie characters and the addressee, who had the same characters in a random order, was asked to place them in order. ToM and other aspects of social cognition were also assessed. Participants used more information when presenting likely unknown than likely known movie characters. They particularly increased their use of physical descriptors, which most often accompanied movie-related information. Interestingly, a significant relationship emerged between our ToM test and the increased amount of information given for the likely unknown characters. These results suggest that speakers use ToM to infer their addressee's likely knowledge and accordingly adapt their referential expressions.
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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 | 0.024 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".