Three distinct components of pragmatic language use: Social conventions, intonation, and world knowledge–based causal reasoning
Bibliographic record
Abstract
Successful communication requires frequent inferences. Such inferences span a multitude of phenomena: from understanding metaphors, to detecting irony and getting jokes, to interpreting intonation patterns. Do all these inferences draw on a single underlying cognitive ability, or does our capacity for nonliteral language comprehension fractionate into dissociable components? Using an approach that has successfully uncovered structure in other domains of cognition, we examined covariation in behavioral performance on diverse nonliteral comprehension tasks across two large samples to search for shared and distinct components of pragmatic language use. In Experiment 1, n = 376 participants each completed an 8 h battery of 20 critical tasks. Controlling for general cognitive ability, an exploratory factor analysis revealed three clusters, which can be post hoc interpreted as corresponding to i) understanding social conventions (critical for phenomena such as indirect requests, conversational implicatures, and irony), ii) interpreting contrastive and emotional intonation patterns, and iii) making causal inferences based on world knowledge. This structure largely replicated in a new sample of n = 400 participants (Experiment 2, preregistered) and was robust to analytic choices. This research uncovers structure in the human communication toolkit and can inform our understanding of pragmatic difficulties in individuals with brain disorders. The hypotheses put forward here about the underlying cognitive abilities can now be evaluated in new behavioral studies, as well as using brain imaging and computational modeling, to continue deciphering the ontology of the component pieces of linguistic and nonverbal communication.
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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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".