1932

Abstract

The past decade has seen the rapid development of a new approach to pragmatics that attempts to integrate insights from formal and experimental semantics and pragmatics, psycholinguistics, and computational cognitive science in the study of meaning: probabilistic pragmatics. The most influential probabilistic approach to pragmatics is the Rational Speech Act (RSA) framework. In this review, I demonstrate the basic mechanics and commitments of RSA as well as some of its standard extensions, highlighting the key features that have led to its success in accounting for a wide variety of pragmatic phenomena. Fundamentally, it treats language as probabilistic, informativeness as gradient, alternatives as context-dependent, and subjective prior beliefs (world knowledge) as a crucial facet of interpretation. It also provides an integrated account of the link between production and interpretation. I highlight key challenges for RSA, which include scalability, the treatment of the boundedness of cognition, and the incremental and compositional nature of language.

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2023-01-17
2024-04-25
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Literature Cited

  1. Achimova A, Scontras G, Stegemann-Philipps C, Lohmann J, Butz MV. 2022. Learning about others: modeling social inference through ambiguity resolution. Cognition 218:104862
    [Google Scholar]
  2. Anderson JR. 1991. Is human cognition adaptive?. Behav. Brain Sci. 14:3471–85
    [Google Scholar]
  3. Attali N, Scontras G, Pearl LS. 2021a. Every quantifier isn't the same: Informativity matters for ambiguity resolution in quantifier-negation sentences. Proc. Soc. Comput. Linguist. 4:1394–95
    [Google Scholar]
  4. Attali N, Scontras G, Pearl LS. 2021b. Pragmatic factors can explain variation in interpretation preferences for quantifier-negation utterances: a computational approach. Proc. Annu. Meet. Cogn. Sci. Soc. 43:917–23
    [Google Scholar]
  5. Augurzky P, Franke M, Ulrich R. 2019. Gricean expectations in online sentence comprehension: an ERP study on the processing of scalar inferences. Cogn. Sci. 43:8e12776
    [Google Scholar]
  6. Beaver D. 2001. Presupposition and Assertion in Dynamic Semantics Stanford, CA: CSLI Publ.
  7. Benz A, Stevens J. 2018. Game-theoretic approaches to pragmatics. Annu. Rev. Linguist. 4:173–91
    [Google Scholar]
  8. Bergen L, Goodman ND, Levy R. 2012. That's what she (could have) said: how alternative utterances affect language use. Proc. Annu. Meet. Cogn. Sci. Soc. 34:120–25
    [Google Scholar]
  9. Bergen L, Levy R, Goodman N. 2016. Pragmatic reasoning through semantic inference. Semant. Pragmat. 9: https://doi.org/10.3765/sp.9.20
    [Crossref] [Google Scholar]
  10. Bohn M, Tessler MH, Merrick M, Frank MC 2021. How young children integrate information sources to infer the meaning of words. Nat. Hum. Behav. 5:81046–54
    [Google Scholar]
  11. Bott L, Noveck I. 2004. Some utterances are underinformative: the onset and time course of scalar inferences. J. Mem. Lang. 51:437–57
    [Google Scholar]
  12. Breheny R, Ferguson HJ, Katsos N. 2013. Taking the Epistemic Step: toward a model of on-line access to conversational implicatures. Cognition 126:3423–40
    [Google Scholar]
  13. Brown-Schmidt S, Konopka AE. 2015. Processes of incremental message planning during conversation. Psychonom. Bull. Rev. 22:3833–43
    [Google Scholar]
  14. Burnett H. 2017. Sociolinguistic interaction and identity construction: the view from game-theoretic pragmatics. J. Sociolinguist. 21:2238–71
    [Google Scholar]
  15. Burnett H. 2019. Signalling games, sociolinguistic variation and the construction of style. Linguist. Philos. 42:5419–50
    [Google Scholar]
  16. Carston R 1998. Informativeness, relevance and scalar implicature. Relevance Theory: Applications and Implications R Carston, S Uchida 179–236 Amsterdam: John Benjamins
    [Google Scholar]
  17. Chambers C, Tanenhaus M, Magnuson J. 2004. Actions and affordances in syntactic ambiguity resolution. J. Exp. Psychol. 30:687–96
    [Google Scholar]
  18. Champollion L, Alsop A, Grosu I 2019. Free choice disjunction as a rational speech act. Proceedings of the 29th Conference on Semantics and Linguistic Theory (SALT 29) K Blake, F Davis, K Lamp, J Rhyne 238–57 Washington, DC: Linguist. Soc. Am.
    [Google Scholar]
  19. Chemla E, Singh R. 2014. Remarks on the experimental turn in the study of scalar implicature, part I. Lang. Linguist. Compass 8:9373–86
    [Google Scholar]
  20. Chierchia G, Fox D, Spector B 2012. Scalar implicature as a grammatical phenomenon. Handbücher zur Sprach-und Kommunikationswissenschaft/Handbooks of Linguistics and Communication Science Semantics, Vol. 3: Semantics C Maienborn, K von Heusinger, P Portner 2297–32 Berlin/Boston: De Gruyter Mouton
    [Google Scholar]
  21. Clark A. 2013. Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behav. Brain Sci. 36:3181–204
    [Google Scholar]
  22. Clark HH. 1992. Arenas of Language Use Chicago: Univ. Chicago Press
  23. Clark HH, Brennan SE 1991. Grounding in communication. Perspectives on Socially Shared Cognition L Resnick, J Levine, S Teasley 127–49 Hyattsville, MD: Am. Psychol. Assoc.
    [Google Scholar]
  24. Clark HH, Marshall C 1981. Definite reference and mutual knowledge. Elements of Discourse Understanding AK Joshi, BL Webber, IA Sag 10–63 New York: Cambridge Univ. Press
    [Google Scholar]
  25. Cohn-Gordon R, Goodman N, Potts C. 2018. Pragmatically informative image captioning with character-level inference. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)439–43 New Orleans, LA: Assoc. Comput. Linguist.
    [Google Scholar]
  26. Cohn-Gordon R, Goodman N, Potts C. 2019. An incremental iterated response model of pragmatics. Proc. Soc. Comput. Linguist. 2:81–90
    [Google Scholar]
  27. Cohn-Gordon R, Qing C 2019. Modeling “non-literal” social meaning. Proceedings of Sinn und Bedeutung 23, Vol. 1 MT Espinal, E Castroviejo, M Leonetti, L McNally, C Real-Puigdollers 301–10 Barcelona: Univ. Autòn. Barcelona
    [Google Scholar]
  28. Cover TM. 1999. Elements of Information Theory New York: John Wiley & Sons
  29. Cummins C, Rohde H. 2015. Evoking context with contrastive stress: effects on pragmatic enrichment. Front. Psychol. https://doi.org/10.3389/fpsyg.2015.01779
    [Crossref] [Google Scholar]
  30. De Neys W, Schaeken W. 2007. When people are more logical under cognitive load: dual task impact on scalar implicature. Exp. Psychol. 54:2128–33
    [Google Scholar]
  31. Degen J, Goodman ND. 2014. Lost your marbles? The puzzle of dependent measures in experimental pragmatics. Proc. Annu. Meet. Cogn. Sci. Soc. 36:397–402
    [Google Scholar]
  32. Degen J, Hawkins RXD, Graf C, Kreiss E, Goodman ND. 2020. When redundancy is useful: a Bayesian approach to ‘overinformative' referring expressions. Psychol. Rev. 127:4591–621
    [Google Scholar]
  33. Degen J, Tanenhaus MK. 2015. Processing scalar implicature: a constraint-based approach. Cogn. Sci. 39:4667–710
    [Google Scholar]
  34. Degen J, Tanenhaus MK. 2016. Availability of alternatives and the processing of scalar implicatures: a visual world eye-tracking study. Cogn. Sci. 40:1172–201
    [Google Scholar]
  35. Degen J, Tessler MH, Goodman ND 2015. Wonky worlds: Listeners revise world knowledge when utterances are odd. Proceedings of the 37th Annual Meeting of the Cognitive Science Society DC Noelle, R Dale, AS Warlaumont, J Yoshimi, T Matlock et al.548–53 Austin, TX: Cogn. Sci. Soc.
    [Google Scholar]
  36. Dionne D, Coppock E. 2022. Complexity versus salience of alternatives in implicature: a cross-linguistic investigation. Glossa Psycholinguist. 1:18
    [Google Scholar]
  37. Dowty D. 1986. The effects of aspectual class on the temporal structure of discourse: semantics or pragmatics?. Linguist. Philos. 9:37–61
    [Google Scholar]
  38. Erk K. 2022. The probabilistic turn in semantics and pragmatics. Annu. Rev. Linguist. 8:101–21
    [Google Scholar]
  39. Ferreira F, Patson ND. 2007. The “good enough” approach to language comprehension. Lang. Linguist. Compass 1:1–271–83
    [Google Scholar]
  40. Fox D, Katzir R. 2011. On the characterization of alternatives. Nat. Lang. Semant. 19:187–107
    [Google Scholar]
  41. Fox D, Spector B. 2018. Economy and embedded exhaustification. Nat. Lang. Semant. 26:11–50
    [Google Scholar]
  42. Frank MC, Goodman ND. 2012. Predicting pragmatic reasoning in language games. Science 336:998
    [Google Scholar]
  43. Frank MC, Goodman ND. 2014. Inferring word meanings by assuming that speakers are informative. Cogn. Psychol. 75:80–96
    [Google Scholar]
  44. Franke M. 2014. Typical use of quantifiers: a probabilistic speaker model. Proc. Annu. Meet. Cogn. Sci. Soc. 36:487–92
    [Google Scholar]
  45. Franke M, Bergen L. 2020. Theory-driven statistical modeling for semantics and pragmatics: a case study on grammatically generated implicature readings. Language 96:2e77–96
    [Google Scholar]
  46. Franke M, Degen J. 2016. Reasoning in reference games: individual- versus population-level probabilistic modeling. PLOS ONE 11:5e0154854
    [Google Scholar]
  47. Franke M, Jäger G. 2016. Probabilistic pragmatics, or why Bayes' rule is probably important for pragmatics. Z. Sprachwiss. 35:13–44
    [Google Scholar]
  48. Fried D, Andreas J, Klein D. 2018a. Unified pragmatic models for generating and following instructions. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)1951–63 Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  49. Fried D, Hu R, Cirik V, Rohrbach A, Andreas J et al. 2018b. Speaker-follower models for vision-and-language navigation. Advances in Neural Information Processing Systems (NeurIPS 2018), Vol. 31 S Bengio, H Wallach, H Larochelle, K Grauman, N Cesa-Bianchi, R Garnett. n.p.: NeurIPS Found. https://proceedings.neurips.cc/paper/2018/file/6a81681a7af700c6385d36577ebec359-Paper.pdf
    [Google Scholar]
  50. Goldberg AE, Ferreira F. 2022. Good-enough language production. Trends Cogn. Sci. 26:4300–11
    [Google Scholar]
  51. Goodman ND, Frank MC. 2016. Pragmatic language interpretation as probabilistic inference. Trends Cogn. Sci. 20:11818–29
    [Google Scholar]
  52. Goodman ND, Lassiter D 2015. Probabilistic semantics and pragmatics: uncertainty in language and thought. The Handbook of Contemporary Semantic Theory S Lappin, C Fox 655–86 Chichester, UK: John Wiley & Sons. , 2nd ed..
    [Google Scholar]
  53. Goodman ND, Stuhlmüller A. 2013. Knowledge and implicature: modeling language understanding as social cognition. Top. Cogn. Sci. 5:1173–84
    [Google Scholar]
  54. Gotzner N, Romoli J. 2022. Meaning and alternatives. Annu. Rev. Linguist. 8:213–34
    [Google Scholar]
  55. Grice HP. 1975. Logic and conversation. Syntax Semant. 3:41–58
    [Google Scholar]
  56. Hagoort P, Hald L, Mastiaansen M, Petersson K. 2004. Integration of word meaning and world knowledge in language comprehension. Science 304:438–41
    [Google Scholar]
  57. Hald LA, Steenbeek-Planting E, Hagoort P. 2007. The interaction of discourse context and world knowledge in online sentence comprehension. Evidence from the N400. Brain Res. 1146:210–18
    [Google Scholar]
  58. Hawkins RD, Franke M, Frank MC, Goldberg AE, Smith K et al. 2022. From partners to populations: a hierarchical Bayesian account of coordination and convention. Psychol. Rev. https://doi.org/10.1037/rev0000348
    [Crossref] [Google Scholar]
  59. Hawkins RD, Gweon H, Goodman ND. 2021. The division of labor in communication: speakers help listeners account for asymmetries in visual perspective. Cogn. Sci. 45:3e12926
    [Google Scholar]
  60. Hawkins RD, Kwon M, Sadigh D, Goodman ND. 2020. Continual adaptation for efficient machine communication. Proceedings of the 24th Conference on Computational Natural Language Learning408–19 Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  61. Hawkins RXD, Stuhlmüller A, Degen J, Goodman ND 2015. Why do you ask? Good questions provoke informative answers. Proceedings of the 37th Annual Meeting of the Cognitive Science Society DC Noelle, R Dale, AS Warlaumont, J Yoshimi, T Matlock et al.878–83 Austin, TX: Cogn. Sci. Soc.
    [Google Scholar]
  62. Heller D, Parisien C, Stevenson S. 2016. Perspective-taking behavior as the probabilistic weighing of multiple domains. Cognition 149:104–20
    [Google Scholar]
  63. Henderson R, McCready E. 2020. Towards functional, agent-based models of dogwhistle communication. Proceedings of the Probability and Meaning Conference (PaM 2020)73–77 Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  64. Herbstritt M, Franke M. 2019. Complex probability expressions & higher-order uncertainty: compositional semantics, probabilistic pragmatics & experimental data. Cognition 186:50–71
    [Google Scholar]
  65. Hirschberg JB. 1985. A theory of scalar implicature PhD Thesis, Univ. Pa. Philadelphia:
  66. Hobbs JR 2019. Word meaning and world knowledge. Semantics: Theories C Maienborn, K von Heusinger, P Portner 154–80 Berlin/Boston: De Gruyter Mouton
    [Google Scholar]
  67. Horn LR. 1972. On the semantic properties of logical operators in English PhD Thesis, Univ. Calif. Los Angeles:
  68. Jasbi M, Waldon B, Degen J 2019. Linking hypothesis and number of response options modulate inferred scalar implicature rate. Front. Psychol. https://doi.org/10.3389/fpsyg.2019.00189
    [Crossref] [Google Scholar]
  69. Kao JT, Bergen L, Goodman ND. 2014a. Formalizing the pragmatics of metaphor understanding. Proc. Annu. Meet. Cogn. Sci. Soc. 36:719–24
    [Google Scholar]
  70. Kao JT, Goodman ND 2015. Let's talk (ironically) about the weather: modeling verbal irony. Proceedings of the 37th Annual Meeting of the Cognitive Science Society DC Noelle, R Dale, AS Warlaumont, J Yoshimi, T Matlock et al.1051–56 Austin, TX: Cogn. Sci. Soc.
    [Google Scholar]
  71. Kao JT, Wu JY, Bergen L, Goodman ND. 2014b. Nonliteral understanding of number words. PNAS 111:3312002–7
    [Google Scholar]
  72. Karttunen L. 1974. Presupposition and linguistic context. Theor. Linguist. 1:181–93
    [Google Scholar]
  73. Katsos N, Bishop DVM. 2011. Pragmatic tolerance: implications for the acquisition of informativeness and implicature. Cognition 120:167–81
    [Google Scholar]
  74. Katzir R. 2007. Structurally-defined alternatives. Linguist. Philos. 30:6669–90
    [Google Scholar]
  75. Kravtchenko E, Demberg V. 2022. Modeling atypicality inferences in pragmatic reasoning. Proc. Annu. Meet. Cogn. Sci. Soc. 44:1918–24
    [Google Scholar]
  76. Kreiss E, Degen J. 2020. Production expectations modulate contrastive inference. Proceedings of the 42nd Annual Conference of the Cognitive Science Society259–65 Austin, TX: Cogn. Sci. Soc.
    [Google Scholar]
  77. Kursat L, Degen J. 2020. Probability and processing speed of scalar inferences is context-dependent. Proceedings of the 42nd Annual Conference of the Cognitive Science Society1236–42 Austin, TX: Cogn. Sci. Soc.
    [Google Scholar]
  78. Kwisthout J, Wareham T, van Rooij I. 2011. Bayesian intractability is not an ailment that approximation can cure. Cogn. Sci. 35:5779–84
    [Google Scholar]
  79. Lakatos I 1970. Falsification and the methodology of scientific research programmes. Criticism and the Growth of Knowledge I Lakatos, A Musgrave 91–196 Cambridge, UK: Cambridge Univ. Press
    [Google Scholar]
  80. Lakoff G 1988. Cognitive semantics. Meaning and Mental Representations U Eco, M Santambrogio, P Violi 119–54 Bloomington: Indiana Univ. Press
    [Google Scholar]
  81. Lassiter D, Goodman ND. 2013. Context, scale structure, and statistics in the interpretation of positive-form adjectives. Proceedings of the 23rd Conference on Semantics and Linguistic Theory (SALT 23)587–610 Washington, DC: Linguist. Soc. Am.
    [Google Scholar]
  82. Lassiter D, Goodman ND. 2017. Adjectival vagueness in a Bayesian model of interpretation. Synthese 194:103801–36
    [Google Scholar]
  83. Lewis D. 1969. Convention: A Philosophical Study Cambridge, MA: Harvard Univ. Press
  84. Lewis D. 1979. Scorekeeping in a language game. J. Philos. Logic 8:1339–59
    [Google Scholar]
  85. Luce RD. 1959. Individual Choice Behavior: A Theoretical Analysis New York: Wiley
  86. Marr D. 1982. Vision: A Computational Investigation into the Human Representation and Processing of Visual Information San Francisco: W.H. Freeman
  87. Matsumoto Y. 1995. The conversational condition on Horn scales. Linguist. Philos. 18:121–60
    [Google Scholar]
  88. Monroe W, Hawkins RX, Goodman ND, Potts C. 2017. Colors in context: a pragmatic neural model for grounded language understanding. Trans. Assoc. Comput. Linguist. 5:325–38
    [Google Scholar]
  89. Montague R. 1970. Universal grammar. Theoria 36:3373–98
    [Google Scholar]
  90. Peeters B 2000. Setting the scene: some recent milestones in the lexicon-encyclopedia debate. The Lexicon-Encyclopedia Interface B Peeters 1–52 New York: Elsevier
    [Google Scholar]
  91. Peloquin BN, Frank M 2016. Determining the alternatives for scalar implicature. Proceedings of the 38th Annual Conference of the Cognitive Science Society A Papafragou, D Grodner, D Mirman, JC Trueswell 319–24 Austin, TX: Cogn. Sci. Soc.
    [Google Scholar]
  92. Potts C, Lassiter D, Levy R, Frank MC. 2016. Embedded implicatures as pragmatic inferences under compositional lexical uncertainty. J. Semant. 33:4755–802
    [Google Scholar]
  93. Qing C, Cohn-Gordon R 2019. Use-conditional meaning in rational speech act models. Proceedings of Sinn und Bedeutung 23, Vol. 2 MT Espinal, E Castroviejo, M Leonetti, L McNally, C Real-Puigdollers 253–66 Barcelona: Univ. Autòn. Barcelona
    [Google Scholar]
  94. Qing C, Franke M 2014. Gradable adjectives, vagueness, and optimal language use: a speaker-oriented model. Proceedings of the 24th Conference on Semantics and Linguistic Theory (SALT 24) T Snider, S D'Antonio, M Weigand 23–41 Washington, DC: Linguist. Soc. Am.
    [Google Scholar]
  95. Qing C, Franke M 2015. Variations on a Bayesian theme: comparing Bayesian models of referential reasoning. Bayesian Natural Language Semantics and Pragmatics H Zeevat, H Schmitz 201–20 Cham, Switz: Springer
    [Google Scholar]
  96. Qing C, Goodman ND, Lassiter D. 2016. A rational speech-act model of projective content. Proceedings of the 38th Annual Meeting of the Cognitive Science Society1110–15 Austin, TX: Cogn. Sci. Soc.
    [Google Scholar]
  97. Roberts C. 2012 (1998. Information structure in discourse: towards an integrated formal theory of pragmatics. Semant. Pragmat. 5:6
    [Google Scholar]
  98. Rohde H, Seyfarth S, Clark B, Jäger G, Kaufmann S 2012. Communicating with cost-based implicature: a game-theoretic approach to ambiguity. Proceedings of the 16th Workshop on the Semantics and Pragmatics of Dialogue S Brown-Schmidt, J Ginzburg, S Larsson 107–16 Paris: SEMDIAL
    [Google Scholar]
  99. Ronai E, Xiang M. 2021. Pragmatic inferences are QUD-sensitive: an experimental study. J. Linguist. 57:4841–70
    [Google Scholar]
  100. Russell B. 2006. Against grammatical computation of scalar implicatures. J. Semant. 23:4361–82
    [Google Scholar]
  101. Russell B. 2012. Probabilistic reasoning and the computation of scalar implicatures PhD Thesis, Brown Univ. Providence, RI:
  102. Sauerland U. 2004. Scalar implicatures in complex sentences. Linguist. Philos. 27:3367–91
    [Google Scholar]
  103. Schöller A, Franke M 2015. Semantic values as latent parameters: surprising few & many. Proceedings of the 25th Conference on Semantics and Linguistic Theory (SALT 25) S D'Antonio, M Moroney, CR Little 143–62 Washington, DC: Linguist. Soc. Am.
    [Google Scholar]
  104. Schuster S, Degen J. 2020. I know what you're probably going to say: listener adaptation to variable use of uncertainty expressions. Cognition 203:104285
    [Google Scholar]
  105. Scontras G, Goodman ND. 2017. Resolving uncertainty in plural predication. Cognition 168:294–311
    [Google Scholar]
  106. Scontras G, Pearl LS. 2021. When pragmatics matters more for truth-value judgments: an investigation of quantifier scope ambiguity. Glossa 6:1110
    [Google Scholar]
  107. Scontras G, Tessler MH, Franke M. 2017. Probabilistic language understanding: an introduction to the Rational Speech Act framework Online course. https://www.problang.org
  108. Shen S, Fried D, Andreas J, Klein D 2019. Pragmatically informative text generation. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)4060–67 Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  109. Sikos L, Venhuizen NJ, Drenhaus H, Crocker MW. 2021. Reevaluating pragmatic reasoning in language games. PLOS ONE 16:3e0248388
    [Google Scholar]
  110. Song Y, Jimenez AH, Scontras G. 2021. Cross-linguistic scope ambiguity: an investigation of English, Spanish, and Mandarin. Proc. Linguist. Soc. Am. 6:1572–86
    [Google Scholar]
  111. Sperber D, Wilson D. 1986. Relevance: Communication and Cognition Cambridge, MA: Harvard Univ. Press
  112. Stalnaker R. 1978. Assertion. Syntax and Semantics, Vol. 9: Pragmatics P Cole 315–32 New York: Academic
    [Google Scholar]
  113. Stevens J, de Marneffe MC, Speer S, Tonhauser J. 2017. Rational use of prosody predicts projectivity in manner adverb utterances. Annu. Meet. Cogn. Sci. Soc. 39:1144–49
    [Google Scholar]
  114. Stiller AJ, Goodman ND, Frank MC. 2015. Ad-hoc implicature in preschool children. Lang. Learn. Dev. 11:2176–90
    [Google Scholar]
  115. Sun C, Breheny R. 2020. Another look at the online processing of scalar inferences: an investigation of conflicting findings from visual-world eye-tracking studies. Lang. Cogn. Neurosci. 35:8949–79
    [Google Scholar]
  116. Sun C, Tian Y, Breheny R. 2018. A link between local enrichment and scalar diversity. Front. Psychol. https://doi.org/10.3389/fpsyg.2018.02092
    [Crossref] [Google Scholar]
  117. Tanenhaus MK, Spivey-Knowlton MJ, Eberhard KM, Sedivy JC 1995. Integration of visual and linguistic information in spoken language comprehension. Science 268:52171632–34
    [Google Scholar]
  118. Tenenbaum JB, Kemp C, Griffiths TL, Goodman ND. 2011. How to grow a mind: statistics, structure, and abstraction. Science 331:60221279–85
    [Google Scholar]
  119. Tessler MH, Goodman ND. 2019. The language of generalization. Psychol. Rev. 126:3395–436
    [Google Scholar]
  120. van Kuppevelt J. 1996. Inferring from topics. Linguist. Philos. 19:4393–443
    [Google Scholar]
  121. van Rooij R, Schulz K. 2004. Exhaustive interpretation of complex sentences. J. Logic Lang. Inform. 13:4491–519
    [Google Scholar]
  122. van Tiel B, Franke M, Sauerland U. 2021. Probabilistic pragmatics explains gradience and focality in natural language quantification. PNAS 118:9e2005453118
    [Google Scholar]
  123. Vul E, Goodman N, Griffiths TL, Tenenbaum JB. 2014. One and done? Optimal decisions from very few samples. Cogn. Sci. 38:4599–637
    [Google Scholar]
  124. Waldon B 2022. A novel probabilistic approach to linguistic imprecision. Measurements, Numerals and Scales: Essays in Honour of Stephanie Solt N Gotzner, U Sauerland 307–27 Cham, Switz: Springer
    [Google Scholar]
  125. Waldon B, Degen J 2020. Modeling behavior in truth value judgment task experiments. Proc. Soc. Comput. Linguist. 3:10–19
    [Google Scholar]
  126. Waldon B, Degen J 2021. Modeling cross-linguistic production of referring expressions. Proc. Soc. Comput. Linguist. 4:206–15
    [Google Scholar]
  127. Warren T, McConnell K. 2007. Investigating effects of selectional restriction violations and plausibility violation severity on eye-movements in reading. Psychon. Bull. Rev. 14:4770–75
    [Google Scholar]
  128. Westerbeek H, Koolen R, Maes A. 2015. Stored object knowledge and the production of referring expressions: the case of color typicality. Front. Psychol. https://doi.org/10.3389/fpsyg.2015.00935
    [Crossref] [Google Scholar]
  129. White J, Mu J, Goodman ND. 2020. Learning to refer informatively by amortizing pragmatic reasoning. Proceedings of the 42nd Annual Conference of the Cognitive Science Society994–1000 Austin, TX: Cogn. Sci. Soc.
    [Google Scholar]
  130. Winograd T. 1972. Understanding natural language. Cogn. Psychol. 3:11–191
    [Google Scholar]
  131. Yoon EJ, Tessler MH, Goodman ND, Frank MC. 2020. Polite speech emerges from competing social goals. Open Mind 4:71–87
    [Google Scholar]
  132. Zondervan AJ. 2010. Scalar implicatures or focus: an experimental approach PhD Thesis, Univ. Utrecht Utrecht, Neth:.
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