--- title: "Predicting Pragmatic Reasoning in Language — Frank 2012" description: "People often understand what a speaker means even when the words alone are ambiguous." authors: ["Michael C. Frank", "Noah D. Goodman"] year: 2012 venue: "Science 336(6084):998" source: "https://doi.org/10.1126/science.1218633" doi: "10.1126/science.1218633" licence: "© AAAS (Science); author-posted manuscript on the author's page, no open licence — readonly" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/frank-2012-predicting-pragmatic-reasoning-language-games/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/frank-2012-predicting-pragmatic-reasoning-language-games/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2012](https://md.research.elamur.ai/free/en/papers/2012/page/2/) / Predicting Pragmatic Reasoning in Language Games # Predicting Pragmatic Reasoning in Language Games **Authors:** Michael C. Frank, [Noah D. Goodman](https://md.research.elamur.ai/free/en/authors/noah-d-goodman/) · **Year:** 2012 · **Venue:** Science 336(6084):998 · **Licence:** © AAAS (Science); author-posted manuscript on the author's page, no open licence — readonly **Links:** [Publisher's page](https://doi.org/10.1126/science.1218633) · [PDF](https://web.stanford.edu/~ngoodman/papers/FrankGoodman-Science2012.pdf) · [DOI 10.1126/science.1218633](https://doi.org/10.1126/science.1218633) · [OpenAlex](https://openalex.org/W1993979041) **Citations:** 859 citations (openalex-snapshot · 2026-10-14) · [148](#cited-by) in our library, of the 140673 papers whose bibliographies we read ## Summary People often understand what a speaker means even when the words alone are ambiguous. Theories of pragmatics explain this informally, but few make precise, testable predictions. Frank and Goodman address this with very simple reference games. Participants see a small set of objects that vary along two dimensions and bet on which object a single word refers to. The model treats the listener as a Bayesian reasoner that combines two parts. The first is a prior over objects, set to their contextual salience, which a separate group of participants measured directly. The second is a likelihood from a speaker who prefers informative words. Informativeness is defined through surprisal, so a word is chosen with probability inversely proportional to how many objects it could describe. Separate groups of participants gave speaker choices, salience ratings and listener interpretations. The combined model has no free parameters and matched listener bets almost perfectly. The paper is an early foundation of the Rational Speech Acts (RSA) framework. It shows that information-theoretic tools can make pragmatic inference measurable and predictable. ## Key points - Three participant groups in a between-subjects design: **speaker** (which word describes an object), **salience** (which object an unknown word refers to, used as the prior) and **listener** (which object a known word such as "blue" refers to). - The speaker likelihood comes from a rational-actor model with surprisal-based utility: P(w | r, C) ∝ |w|⁻¹, where |w| is the number of objects in context that the word fits. - The model's speaker predictions correlated with human speaker bets at r = .98. Listener predictions correlated with listener bets at r = .99, and still at r = .87 when predictions of 0 and 100 were left out. - Salience judgments on their own did not predict listener judgments (r = .19, p = .40). The good fit appears only when salience is combined with speaker informativeness. - The model unifies earlier work on disambiguation models, game-theoretic signaling and referring-expression generation. It appeared as a short report in Science 336(6084), 2012. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/) ## Cited by Cited by 148 papers of our library cite it (of the 140673 whose bibliographies we read); 25 of them have a page here, 123 not yet. - [Tell Me Everything You Know: A Conversation Update System for the Rational Speech Acts Framework](https://md.research.elamur.ai/free/en/papers/anderson-2021-tell-me-everything-you-know/) - [Experience Grounds Language](https://md.research.elamur.ai/free/en/papers/bisk-2020-experience-grounds-language/) - [Toward Gender-Inclusive Coreference Resolution: An Analysis of Gender and Bias Throughout the Machine Learning Lifecycle*](https://md.research.elamur.ai/free/en/papers/cao-2021-gender-inclusive-coreference-resolution-analysis/) - [Investigating the distribution of some (but not all) implicatures using corpora and web-based methods](https://md.research.elamur.ai/free/en/papers/degen-2015-investigating-distribution-some-but-not/) - [Unified Pragmatic Models for Generating and Following Instructions](https://md.research.elamur.ai/free/en/papers/fried-2018-unified-pragmatic-models-generating-following/) - [Pragmatics in Language Grounding: Phenomena, Tasks, and Modeling Approaches](https://md.research.elamur.ai/free/en/papers/fried-2023-pragmatics-language-grounding-phenomena-tasks/) - [Towards Pragmatic Production Strategies for Natural Language Generation Tasks](https://md.research.elamur.ai/free/en/papers/giulianelli-2022-pragmatic-production-strategies-natural-language/) - [Pragmatically Informative Image Captioning with Character-Level Inference](https://md.research.elamur.ai/free/en/papers/gordon-2018-pragmatically-informative-image-captioning-character/) - [An Incremental Iterated Response Model of Pragmatics](https://md.research.elamur.ai/free/en/papers/gordon-2019-incremental-iterated-response-model-pragmatics/) - [Expectations over Unspoken Alternatives Predict Pragmatic Inferences](https://md.research.elamur.ai/free/en/papers/hu-2023-expectations-over-unspoken-alternatives-predict/) - [A fine-grained comparison of pragmatic language understanding in humans and language models](https://md.research.elamur.ai/free/en/papers/hu-2023-fine-grained-comparison-pragmatic-language/) - [Will I Sound Like Me? Improving Persona Consistency in Dialogues through Pragmatic Self-Consciousness](https://md.research.elamur.ai/free/en/papers/kim-2020-will-i-sound-like-me/) - [Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes](https://md.research.elamur.ai/free/en/papers/kim-2021-perspective-taking-pragmatics-generating-empathetic/) - [GeDi: Generative Discriminator Guided Sequence Generation](https://md.research.elamur.ai/free/en/papers/krause-2021-gedi-generative-discriminator-guided-sequence/) - [Referring Expressions with Rational Speech Act Framework: A Probabilistic Approach](https://md.research.elamur.ai/free/en/papers/le-2022-referring-expressions-rational-speech-act/) - [Evaluating statistical language models as pragmatic reasoners](https://md.research.elamur.ai/free/en/papers/lipkin-2023-evaluating-statistical-language-models-pragmatic/) - [Colors in Context: A Pragmatic Neural Model for Grounded Language Understanding](https://md.research.elamur.ai/free/en/papers/monroe-2017-colors-context-pragmatic-neural-model/) - [DIDECO: An Annotated Dataset for Intent Detection in Digital Communications](https://md.research.elamur.ai/free/en/papers/popovic-2026-dideco-annotated-dataset-intent-detection/) - [PragmatiCQA: A Dataset for Pragmatic Question Answering in Conversations](https://md.research.elamur.ai/free/en/papers/qi-2023-pragmaticqa-dataset-pragmatic-question-answering/) - [Pragmatically Informative Text Generation](https://md.research.elamur.ai/free/en/papers/shen-2019-pragmatically-informative-text-generation/) - [Implicatures and Nested Beliefs in Approximate Decentralized-POMDPs](https://md.research.elamur.ai/free/en/papers/vogel-2013-implicatures-nested-beliefs-approximate-decentralized/) - [RSA-Control: A Pragmatics-Grounded Lightweight Controllable Text Generation Framework](https://md.research.elamur.ai/free/en/papers/wang-2024-rsa-control-pragmatics-grounded-lightweight/) - [Do I know what I want to say? Modeling meaning uncertainty in RSA](https://md.research.elamur.ai/free/en/papers/wang-2026-do-i-know-what-i/) - [Know What You Don't Know: Modeling a Pragmatic Speaker that Refers to Objects of Unknown Categories](https://md.research.elamur.ai/free/en/papers/zarrie-2019-know-what-you-don-t/) - [GRICE: A Grammar-based Dataset for Recovering Implicature and Conversational rEasoning](https://md.research.elamur.ai/free/en/papers/zheng-2021-grice-grammar-based-dataset-recovering/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{frank2012predicting, title = {{Predicting Pragmatic Reasoning in Language Games}}, author = {Frank, Michael C. and Goodman, Noah D.}, booktitle = {Science 336(6084):998}, year = {2012}, doi = {10.1126/science.1218633}, url = {https://doi.org/10.1126/science.1218633} } ``` - [← previous Predicting Learner Levels for Online Exercises of Hebrew](https://md.research.elamur.ai/free/en/papers/aclbulk-2012-dickinson-predicting-learner-levels-online-exercises/) - [next → Re-examining Machine Translation Metrics for Paraphrase Identification](https://md.research.elamur.ai/free/en/papers/madnani-2012-re-examining-machine-translation-metrics/) - [↑ back to papers of 2012](https://md.research.elamur.ai/free/en/papers/2012/page/2/) ## References 9 references, 0 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. H. Grice, in Syntax and Semantics, vol. 3, P. Cole and J. Morgan (Academic Press, New York, NY, 1975), 41–58. [find](https://scholar.google.com/scholar?q=H%2E+Grice%2C+in+Syntax+and+Semantics%2C+vol%2E+3%2C+P%2E+Cole+and+J%2E+Morgan+%28Academic+Press%2C+New+York%2C+NY%2C+1975%29%2C+41%E2%80%9358%2E) 2. D. Sperber, D. Wilson, Relevance: Communication and Cognition (Harvard University Press, Cambridge, MA, 1986). [find](https://scholar.google.com/scholar?q=D%2E+Sperber%2C+D%2E+Wilson%2C+Relevance%3A+Communication+and+Cognition+%28Harvard+University+Press%2C+Cambridge%2C+MA%2C+1986%29%2E) 3. H. Clark, Using Language (Cambridge University Press, Cambridge, UK, 1996). [find](https://scholar.google.com/scholar?q=H%2E+Clark%2C+Using+Language+%28Cambridge+University+Press%2C+Cambridge%2C+UK%2C+1996%29%2E) 4. L. Wittgenstein, Philosophical Investigations (Blackwell Publishers, Oxford, UK, 1953). [find](https://scholar.google.com/scholar?q=L%2E+Wittgenstein%2C+Philosophical+Investigations+%28Blackwell+Publishers%2C+Oxford%2C+UK%2C+1953%29%2E) 5. H. Clark, R. Schreuder, S. Buttrick, J. Verb. Learn. Verb. Behav. 22, 245–258 (1983). [find](https://scholar.google.com/scholar?q=H%2E+Clark%2C+R%2E+Schreuder%2C+S%2E+Buttrick%2C+J%2E+Verb%2E+Learn%2E+Verb%2E+Behav%2E+22%2C+245%E2%80%93258+%281983%29%2E) 6. F. Xu, J. Tenenbaum, Psychol. Rev. 114, 245–272 (2007). [find](https://scholar.google.com/scholar?q=F%2E+Xu%2C+J%2E+Tenenbaum%2C+Psychol%2E+Rev%2E+114%2C+245%E2%80%93272+%282007%29%2E) 7. S. Rosenberg, B. Cohen, Science 145, 1201–1203 (1964). [find](https://scholar.google.com/scholar?q=S%2E+Rosenberg%2C+B%2E+Cohen%2C+Science+145%2C+1201%E2%80%931203+%281964%29%2E) 8. A. Benz, G. Jäger, R. Van Rooij, Eds., Game theory and pragmatics (Palgrave Macmillan, Hampshire, UK, 2005). [find](https://scholar.google.com/scholar?q=A%2E+Benz%2C+G%2E+Ja%CC%88ger%2C+R%2E+Van+Rooij%2C+Eds%2E%2C+Game+theory+and+pragmatics+%28Palgrave+Macmillan%2C+Hampshire%2C+UK%2C+2005%29%2E) 9. R. Dale, E. Reiter, Cognit. Sci. 19, 233–263 (1995). [find](https://scholar.google.com/scholar?q=R%2E+Dale%2C+E%2E+Reiter%2C+Cognit%2E+Sci%2E+19%2C+233%E2%80%93263+%281995%29%2E)