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Variations on a Bayesian Theme: Comparing Bayesian Models of Referential Reasoning

  • Ciyang QingEmail author
  • Michael Franke
Chapter
Part of the Language, Cognition, and Mind book series (LCAM, volume 2)

Abstract

Recent developments in Bayesian experimental pragmatics have received much attention. The Rational Speech Act (RSA) model formalizes core concepts of traditional pragmatic theories quantitatively and makes predictions that fit empirical data nicely. In this paper, we analyze the RSA model and its relation to closely related game theoretic approaches, by spelling out its belief, goal and action components. We introduce some alternatives motivated from the game theoretic tradition and compare models incorporating these alternatives systematically to the original RSA model, using Bayesian model comparison, in terms of their ability to predict relevant empirical data. The result suggests that the RSA model could be adapted and extended to improve its predictive power, in particular by taking speaker preferences into account.

Keywords

Referential expressions Scalar implicature Pragmatic reasoning  Referential game Bayesian pragmatics Rational speech-act theory Game-theoretic pragmatics Experimental pragmatics Bayesian analysis Model comparison 

Notes

Acknowledgments

We are indebted to Judith Degen, Michael C. Frank, Noah D. Goodman and Daniel Lassiter, two anonymous reviewers, and the audience of the ESSLLI workshop “Bayesian Natural Language Semantics and Pragmatics” for stimulating feedback and discussion. Many thanks also to Henk Zeevat and Hans-Christian Schmitz for organizing mentioned workshop, and to Will Frager for help realizing our experiments. Michael Franke gratefully acknowledges financial support by NWO-VENI grant 275-80-004.

References

  1. Benz, A., & van Rooij, R. (2007). Optimal assertions, and what they implicate. A uniform game theoretic approach. Topoi, 26(1), 63–78. doi: 10.1007/s11245-006-9007-3.CrossRefGoogle Scholar
  2. Bergen, L., Goodman, N. D., & Levy, R. (2012). That’s what she (could have) said: How alternative utterances affect language use. In N. Miyake, D. Peebles, & R. P. Cooper (Eds.), Proceedings of the 34th Annual Conference of the Cognitive Science Society (pp. 120–125). Austin: Cognitive Science Society.Google Scholar
  3. Degen, J., & Franke, M. (2012). Optimal reasoning about referential expressions. In S. Brown-Schmidt, J. Ginzburg, & S. Larsson (Eds.), Proceedings of the 16th Workshop on the Semantics and Pragmatics of Dialogue (SeineDial: SemDial 2012) (pp. 2–11). Paris: France.Google Scholar
  4. Dickey, J. M., & Lientz, B. P. (1970). The weighted likelihood ratio, sharp hypotheses about chances, the order of a Markov chain. The Annals of Mathematical Statistics, 41(1), 214–226.CrossRefGoogle Scholar
  5. Frank, M. C., & Goodman, N. D. (2012). Predicting pragmatic reasoning in language games. Science, 336(6084), 998. doi: 10.1126/science.1218633.CrossRefGoogle Scholar
  6. Frank, M. C., Goodman, N. D., Lai, P., & Tenenbaum, J. B. (2009). Informative communication in word production and word learning. In N. Taatgen & H. van Rijn (Eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society (pp. 1228–1233). Austin: Cognitive Science Society.Google Scholar
  7. Franke, M. (2011). Quantity implicatures, exhaustive interpretation, and rational conversation. Semantics and Pragmatics, 4(1), 1–82. doi: 10.3765/sp.4.1.Google Scholar
  8. Franke, M., & Jäger, G. (2014). Pragmatic back-and-forth reasoning. In S. Pistoia Reda (Ed.), Semantics, pragmatics and the case of scalar implicatures (pp. 170–200). New York: Palgrave MacMillan. doi: 10.1057/9781137333285.0011.Google Scholar
  9. Gatt, A., van Gompel, R. P. G., van Deemter, K., & Kramer, E. (2013). Are we Bayesian referring expression generators? In M. Knauff, M. Pauen, N. Sebanz, & I. Wachsmuth (Eds.), Proceedings of the 35th Annual Conference of the Cognitive Science Society (pp. 1228–1233). Austin: Cognitive Science Society.Google Scholar
  10. Goodman, N. D., & Stuhlmüller, A. (2013). Knowledge and implicature: Modeling language understanding as social cognition. Topics in Cognitive Science, 5(1), 173–184. doi: 10.1111/tops.12007.CrossRefGoogle Scholar
  11. Grice, H. (1975). Logic and conversation. In P. Cole & J. L. Morgan (Eds.), Syntax and semantics 3: Speech acts (pp. 41–58). New York: Academic Press.Google Scholar
  12. Jäger, G. (2013). Rationalizable signaling. Erkenntnis, 79(4), 673–706. doi: 10.1007/s10670-013-9462-3.Google Scholar
  13. Jaynes, E. T. (2003). Probability theory: the logic of science. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  14. Jeffreys, H. (1961). Theory of probability (3rd ed.). Oxford: Oxford University Press.Google Scholar
  15. Kass, R. E., & Raftery, A. E. (1995). Bayes factors. Journal of the American Statistical Association, 90(430), 773–795. doi: 10.2307/2291091.CrossRefGoogle Scholar
  16. Kramer, E., & van Deemter, K. (2012). Computational generation of referring expressions: A survey. Computational Linguistics, 38(1), 173–218. doi: 10.1162/coli_a_00088.
  17. Rabin, M. (1990). Communication between rational agents. Journal of Economic Theory, 51(1), 144–170. doi: 10.1016/0022-0531(90)90055-O.CrossRefGoogle Scholar
  18. Stalnaker, R. (2005). Saying and meaning, cheap talk and credibility. In A. Benz, G. Jäger, & R. van Rooij (Eds.), Game theory and pragmatics (pp. 83–100). New York: Palgrave MacMillan.Google Scholar
  19. Stiller, A., Goodman, N. D., & Frank, M. C. (2011). Ad-hoc scalar implicature in adults and children. In L. Carlson, C. Hölscher, & T. F. Shipley (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society (pp. 2134–2139). Austin: Cognitive Science Society.Google Scholar
  20. Sutton, R. S., & Barto, A. G. (1998). Reinforcement learning: An introduction. Cambridge: MIT Press.Google Scholar
  21. Vandekerckhove, J., Matzke, D., & Wagenmakers, E.-J. (in press). Model comparison and the principle of parsimony. In J. Busemeyer, J. Townsend, Z. J. Wang, & A. Eidels (Eds.), Oxford handbook of computational and mathematical psychology. Oxford: Oxford University Press.Google Scholar
  22. Wagenmakers, E.-J., Lodewyckx, T., Kuriyal, H., & Grasman, R. (2010). Bayesian hypothesis testing for psychologists: A tutorial on the Savage-Dickey method. Cognitive Psychology, 60(3), 158–189. doi: 10.1016/j.cogpsych.2009.12.001.CrossRefGoogle Scholar
  23. Worthy, D. A., Maddox, W. T., & Markman, A. B. (2008). Ratio and difference comparisons of expected reward in decision-making tasks. Memory & Cognition, 36(8), 1460–1469. doi: 10.3758/MC.36.8.1460.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.LinguisticsStanford UniversityStanfordUSA
  2. 2.LinguisticsUniversity of TubingenTubingenGermany

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