No one-to-one mapping between typologies of pragmatic relations and models of pragmatic processing: a case study with mentalizing
Bibliographic record
Abstract
In this article, we argue that the growth of research in cognitively and experimentally oriented pragmatics in the last two decades has rested on two epistemological assumptions: that theoretical-pragmatic notions such as 'implicature', 'metaphor' and 'irony' correspond to distinct types of pragmatic inferences, and that each theoretical-pragmatic characterization of a certain type of inference corresponds to one and only one cognitive model of processing in the mind. We review the foundations of these assumptions and we problematize them based on (i) a conceptual argument that notions such as 'implicature' and 'irony' are originally meant as relations between propositions rather than types of inferences, and (ii) on recent experimental evidence which suggests that whether mentalizing is employed in pragmatic processing or not is not a function of the type of pragmatic relation, but rather it depends on situation-specific considerations and characteristics of the interlocutor, such as age and neurotype. These considerations call for a new understanding of the role of experimental evidence in the evaluation of pragmatic theories.This article is part of the theme issue 'At the heart of human communication: new views on the complex relationship between pragmatics and Theory of Mind'.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".