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Semantic values as latent parameters: Testing a fixed threshold hypothesis for cardinal readings of few & many

  • Anthea Schöller and Michael Franke ORCID logo EMAIL logo
From the journal Linguistics Vanguard

Abstract

Certain uses of vague quantifiers few and many intuitively compare a true quantity to a priori expectations about that quantity. A concrete proposal for the truth conditions of such readings stipulates a contextually-stable threshold on a contextually-variable representation of a priori expectations (Clark, H. H. 1991. Words, the world, and their possibilities. In Lockhead G. R. & Pomerantz J. R. (eds.), The perception of structure: Essays in honor of Wendell R Garner, 263–277. Washington, DC: American Psychological Association; Fernando, T. & H. Kamp. 1996. Expecting many. In Galloway T. & Spence J. (eds.), Proceedings of SALT VI, 53–68. Ithaca, NY: Cornell University.) The main goal of this paper is to introduce data-driven computational modeling as a means to implement and test complex semantic theories of this kind, which may be hard to assess based on solitary introspection of meaning intuitions. Based on an empirical measure of a priori expectations, we use Bayesian inference to estimate likely values of the latent threshold parameters given empirical data from production and comprehension tasks. We demonstrate how posterior inference and statistical model comparison can help assess the plausibility of the fixed threshold hypothesis.

Appendix

A Experimental material

  1. book – A friend’s favorite book has been published only recently (and has few/many pages). – How many pages do you think the book has? – intervals: 0–40, 41–80, 81–120, 121–160, 161–200, 201–240, 241–280, 281–320, 321–360, 361–400, 401–440, 441–480, 481–520, 521–560, 560 or more

  2. bus – Vehicle No. 102 is a school bus (which has seats for few/many passengers). – How many passengers do you think can sit in Vehicle No. 102? – intervals: 0–4, 5–9, 10–14, 15–19, 20–24, 25–29, 30–34, 35–39, 40–44, 45–49, 50–54, 55–59, 60–64, 65–69, 70 or more

  3. calls – Lisa is a woman from the US (who made few/many phone calls last week). – How many phone calls do you think Lisa made last week? – intervals: 0–4, 5–9, 10–14, 15–19, 20–24, 25–29, 30–34, 35–39, 40–44, 45–49, 50–54, 55–59, 60–64, 65–69, 70 or more

  4. class – Erin is a first grade student in primary school. (There are few/many children in Erins class.) – How many children do you think are in Erin’s class? – intervals: 0–2, 3–5, 6–8, 9–11, 12–14, 15–17, 18–20, 21–23, 24–26, 27–29, 30–32, 33–35, 36–38, 39–41, 42 or more

  5. coffee – Andy is man from the US (who drank few/many cups of coffee last week). – How many cups of coffee do you think Andy drank last week? – intervals: 0–1, 2–3, 4–5, 6–7, 8–9, 10–11, 12–13, 14–15, 16–17, 18–19, 20–21, 22–23, 24–25, 26–27, 28 or more

  6. cook – Tony is a man from the US (who cooked himself few/many meals at home last month). – How many meals do you think Tony cooked himself at home last month? – intervals: 0–3, 4–7, 8–11, 12–15, 16–19, 20–23, 24–27, 28–31, 32–35, 36–39, 40–43, 44–47, 48–51, 52–55, 56 or more

  7. facebook – Judith is a woman from the US (who has few/many Facebook friends). – How many Facebook friends do you think Judith has? – intervals: 0–69, 70–139, 140–209, 210–279, 280–349, 350–419, 420–489, 490–559, 560–629, 630–699, 700–769, 770–839, 840–909, 910–979, 980 or more

  8. friends – Lelia is a woman from the US (who has few/many friends). – How many friends do you think Lelia has? – intervals: 0–1, 2–3, 4–5, 6–7, 8–9, 10–11, 12–13, 14–15, 16–17, 18–19, 20–21, 22–23, 24–25, 26–27, 28 or more

  9. hair – Betty is a woman from the US (who washed her hair few/many times last month). – How many times do you think Betty washed her hair last month? – intervals: 0–2, 3–5, 6–8, 9–11, 12–14, 15–17, 18–20, 21–23, 24–26, 27–29, 30–32, 33–35, 36–38, 39–41, 42 or more

  10. movie – Nick is a man from the US (who saw few/many movies last year). – How many movies do you think Nick saw last year? – intervals: 0–2, 3–5, 6–8, 9–11, 12–14, 15–17, 18–20, 21–23, 24–26, 27–29, 30–32, 33–35, 36–38, 39–41, 42 or more

  11. poem – A friend wants to read you her favorite poem (which has few/many lines). – How many lines do you think the poem has? – intervals: 0–3, 4–7, 8–11, 12–15, 16–19, 20–23, 24–27, 28–31, 32–35, 36–39, 40–43, 44–47, 48–51, 52–55, 56 or more

  12. restaurants – Sarah is a woman from the US (who went to few/many restaurants last year). – To how many restaurants do you think Sarah went last year? – intervals: 0–3, 4–7, 8–11, 12–15, 16–19, 20–23, 24–27, 28–31, 32–35, 36–39, 40–43, 44–47, 48–51, 52–55, 56 or more

  13. shoes – Melanie is a woman from the US (who owns few/many pairs of shoes). – How many pairs of shoes do you think Melanie owns? – intervals: 0–2, 3–5, 6–8, 9–11, 12–14, 15–17, 18–20, 21–23, 24–26, 27–29, 30–32, 33–35, 36–38, 39–41, 42 or more

  14. tshirts – Liam is a man from the US (who has few/many T-shirts). – How many T-shirts do you think Liam has? – intervals: 0–2, 3–5, 6–8, 9–11, 12–14, 15–17, 18–20, 21–23, 24–26, 27–29, 30–32, 33–35, 36–38, 39–41, 42 or more

References

Chemla, E. & R. Singh. 2014. Remarks on the experimental turn in the study of scalar implicature (part I & II). Language and Linguistics Compass 8(9). 373–386, 387–399.10.1111/lnc3.12080Search in Google Scholar

Clark, H. H. 1991. Words, the world, and their possibilities. In G. R. Lockhead & J. R. Pomerantz (eds.), The perception of structure: Essays in honor of Wendell R. Garner, 263–277. Washington, DC: American Psychological Association .10.1037/10101-016Search in Google Scholar

Cohen, A. 2001. Relative readings of Many, Often, and generics. Natural Language Semantics 9. 41–67.10.1023/A:1017913406219Search in Google Scholar

Eckardt, R. 1999. Focus and nominal quantifiers. In P. Bosch & R. van der Sand (eds.), Focus, 166–187. Cambridge: Cambridge University Press.Search in Google Scholar

Fernando, T. & H. Kamp. 1996. Expecting many. In T. Galloway & J. Spence (eds.), Proceedings of SALT VI, 53–68. Ithaca, NY: Cornell University.10.3765/salt.v6i0.2761Search in Google Scholar

Franke, M. 2012. On scales, salience & referential language use. In M. Aloni, F. Roelofsen, & K. Schulz (eds.), Amsterdam colloquium 2011, Lecture Notes in Computer Science, 311–320. Springer: Berlin, Heidelberg.10.1007/978-3-642-31482-7_32Search in Google Scholar

Franke, M. 2016. Task types, link functions & probabilistic modeling in experimental pragmatics. In F. Salfner & U. Sauerland (eds.), Proceedings of trends in experimental pragmatics, 56–63.Search in Google Scholar

Franke, M., F. Dablander, A. Schöller, E. D. Bennett, J. Degen, M. H. Tessler, J. Kao & N.D. Goodman. 2016. What does the crowd believe? A hierarchical approach to estimating subjective beliefs from empirical data. In Proceedings of CogSci 38. 2669–2674.Search in Google Scholar

Franke, M. & G. Jäger. 2016. Probabilistic pragmatics, or why Bayes’ rule is probably important for pragmatics. Zeitschrift für Sprachwissenschaft 3(1). 3–44.10.1515/zfs-2016-0002Search in Google Scholar

Gelman, A. & D. B. Rubin. 1992. Inference from iterative simulation using multiple sequences. Statistical Science 7(4). 457–472.10.1214/ss/1177011136Search in Google Scholar

Goodman, N. D. & M. C. Frank. 2016. Pragmatic language interpretation as probabilistic inference. Trends in Cognitive Sciences 20(11). 818–829.10.1016/j.tics.2016.08.005Search in Google Scholar

Griffiths, T. L. & J. B Tenenbaum. 2006. Optimal predictions in everyday cognition. Psychological Science 17(9). 767–773.10.1111/j.1467-9280.2006.01780.xSearch in Google Scholar

Hackl, M. 2009. On the grammar and processing of proportional quantifiers. Most versus more than half. Natural Language Semantics 17(1). 63–98.10.1007/s11050-008-9039-xSearch in Google Scholar

Hörmann, H. 1983. Was tun die Wörter miteinander im Satz? oder, Wieviele sind einige, mehrere und ein paar?. Göttingen: Verlag für Psychologie .Search in Google Scholar

Kao, J. T., J. Y. Wu, L. Bergen & N. D Goodman. 2014. Nonliteral understanding of number words. Proceedings of the National Academy of Sciences 111(33). 12002–12007.10.1073/pnas.1407479111Search in Google Scholar

Kennedy, C. 2007. Vagueness and grammar: The semantics of relative and absolute gradable adjectives. Linguistics and Philosophy 30(1). 1–45.10.1007/s10988-006-9008-0Search in Google Scholar

Kennedy, C. & L McNally. 2005. Scale structure, degree modification, and the semantics of gradable predicates. Language 81(2). 345–381.10.1353/lan.2005.0071Search in Google Scholar

Kruschke, J. 2014. Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan. Amsterdam: Academic Press .10.1016/B978-0-12-405888-0.00008-8Search in Google Scholar

Lassiter, D. & N. D. Goodman. 2015. Adjectival vagueness in a Bayesian model of interpretation. Synthese . 1–36.10.1007/s11229-015-0786-1Search in Google Scholar

Moxey, L. M. & A. J. Sanford. 2000. Communicating quantities: A review of psycholinguistic evidence of how expressions determine perspectives. Applied Cognitive Psychology 14(3). 237–255.10.1002/(SICI)1099-0720(200005/06)14:3<237::AID-ACP641>3.0.CO;2-RSearch in Google Scholar

Partee, B. 1989. Many quantifiers. In J. Powers & K. de Jong (eds.), 5th Eastern States Conference on Linguistics (ESCOL), 383–402. Columbus: Ohio State University .Search in Google Scholar

Plummer, M. 2003. JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. In Kurt Hornik, Friedrich Leisch & Achim Zeileis (eds.), Proceedings of the 3rd International Workshop on Distributed Statistical Computing.Search in Google Scholar

Plummer, M. 2008. Penalized loss functions for Bayesian model comparison. Biostatistics 9(3). 523–539.10.1093/biostatistics/kxm049Search in Google Scholar

Qing, C. & M. Franke. 2014. Gradable adjectives, vagueness, and optimal language use: A speaker-oriented model. Linguistic society of America SALT 24. 23–41.10.3765/salt.v24i0.2412Search in Google Scholar

Quine, W. V. O. 1951. Two dogmas of empiricism. The Philosophical Review 60. 20–43.10.2307/2181906Search in Google Scholar

Romero, M. 2015. The conservativity of many. In Proceedings of the 20th Amsterdam Colloquium. 20–29.Search in Google Scholar

Solt, S. 2009. The semantics of adjectives of quantity. The City University of New York, Ph. D. thesis.Search in Google Scholar

Solt, S. 2011. Vagueness in quantity: Two case studies from a linguistic perspective. Understanding vagueness. Logical, philosophical and linguistic perspectives, 157–174. College Publications Understanding vagueness. Logical, philosophical and linguistic perspectives.Search in Google Scholar

Spiegelhalter, D. J., N. G. Best, B. P. Carlin & A. Van Der Linde. 2002. Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 64(4). 583–639.10.1111/1467-9868.00353Search in Google Scholar

Vehtari, A. & J. Ojanen. 2012. A survey of Bayesian predictive methods for model assessment, selection and comparison. Statistics Surveys 6. 142–228.10.1214/12-SS102Search in Google Scholar

Westerståhl, D. 1985. Logical constants in quantifier languages. Linguistics and Philosophy 8(4). 387–413.10.1007/BF00637410Search in Google Scholar

Received: 2016-8-29
Accepted: 2017-3-27
Published Online: 2017-8-24

© 2017 Walter de Gruyter GmbH, Berlin/Boston

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