Competence by default: do listeners assume that speakers are knowledgeable when computing conversational inferences?
Bibliographic record
Abstract
Abstract When engaged in conversation, do listeners make default assumptions about the epistemic states of speakers? According to some accounts, when listeners hear a sentence like “Sarah solved some of the math problems,” they infer by default that speakers believe that the stronger statement involving “all” is false (i.e. that Sarah did not solve all of the problems). However, drawing on tests of reading time, eye tracking, and manipulations of cognitive load, multiple studies have argued that this form of inference (i.e. strong scalar implicature) is not computed by default. In this study, while acknowledging this claim, we explore whether important subprocesses of implicature might nevertheless involve default inferences. In particular, we tested whether listeners assume by default that speakers are knowledgeable about alternative utterances that are left unsaid—a critical precondition for computing strong scalar implicatures. To do this, we tested 60 English-speaking participants who heard utterances made by either knowledgeable speakers or ignorant speakers. In addition, half of these participants were placed under cognitive load using a dot-array memory task. We found that participants placed under load over-computed implicatures when speakers were ignorant, as though assuming that they were knowledgeable by default.
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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 | 0.008 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".