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When pragmatics matters more for truth-value judgments: An investigation of quantifier scope ambiguity

Paper · 2021 · Gregory Scontras and Lisa S. Pearl

Summary

Truth-value judgment tasks show that five-year-olds often reject sentences like Every horse didn't jump over the fence in a "not-all" situation, while adults accept them. Researchers have not agreed on the cause. Children may lack the inverse-scope reading or struggle to process it, which would be grammatical factors. Or they may find the sentence an odd thing to say, which would be a pragmatic factor. Scontras and Pearl build a computational cognitive model of how a listener resolves the scope ambiguity in context. In the model, endorsing a sentence works like a speaker's choice: would I say this here? The model separates the influence of scope-access (processing) factors from pragmatic factors such as informativity, the question under discussion and expectations about the world. Pragmatic factors explain children's non-adult judgments better than processing does. The same machinery then accounts for adults' child-like rejection of two-not sentences, where a strong surface-scope bias is predicted. This points to continuity between child and adult ambiguity resolution. It also warns that judgment data cannot be read as direct evidence of grammar until the pragmatics is understood.

Key points

  • The case study is universal quantifier plus negation. The surface reading (∀ > ¬) means "none", and the inverse reading (¬ > ∀) means "not all".
  • In not-all scenarios, adults endorse every-not sentences 90–100% of the time, while five-year-olds do so only about 10–20% of the time (Musolino 1998; 2006; Viau et al. 2010).
  • The model is one of recursive speaker–listener reasoning. It treats grammatical processing (access to each scope) and pragmatic pressures (informativity, truth probability, context) as separable parameters, so each one's contribution can be quantified.
  • For two-not sentences, the model needs a strong surface-scope bias, but for every-not it does not. This fits Conroy (2008): adults try surface scope first, five-year-olds seem to have this bias, and four-year-olds seem not to.
  • Attali et al. (2021) reused the same architecture with fixed parameters for some-not and no-not sentences and confirmed its predictions with a paraphrase-endorsement task.

Interesting ideas

  • I1. A yes/no truth judgment can be modelled as a production decision: would a cooperative speaker choose this sentence for the known state of affairs? This treats it as more than a check of literal truth.
  • I2. Each scope reading of an ambiguous sentence can be kept as a separate logical form with its own access weight. The final interpretation is then chosen by context, instead of committing early to one parse.
  • I3. Observed behavior can be broken down into named factors (scope access, question under discussion, world prior) whose individual contributions can be measured. This makes it possible to say why a judgment came out as it did.
  • I4. Generality can be tested by keeping parameters fixed while moving to new constructions (every-not → some-not, no-not). This guards against fitting each case separately.
  • I5. An expectation about how likely outcomes are, such as whether horses usually succeed at jumping, shapes which reading feels informative.

For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

  • I1 (answer): Readers answering "is this statement about the fable right?" could ask whether the statement is an apt description of the world of the text, not only whether it is true under some reading.
  • I2 (frames): Quantifier–negation frames could store both ∀>¬ and ¬>∀ readings with an explainable prior, and leave the final pick to the question under discussion.
  • I3 (learn): Answer errors could be analysed in the same way, separating grammar or processing failures from pragmatic or context mismatches before choosing which component to repair.
  • I4 (learn): Evaluations could freeze pragmatic settings tuned on one construction and check them on unseen quantifier patterns as a held-out control.
  • I5 (world): World-state priors from compiled knowledge could feed the choice between readings, such as "none" versus "not all", when a story leaves it open.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of California, IrvineIrvine · United States29 works · 36,049 citations

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 9,296 worksLanguage and Linguistics · 9,206 worksPsychology · 798 worksDevelopmental and Educational Psychology · 260 works

Cite

Cite as Gregory Scontras and Lisa S. Pearl. 2021. When pragmatics matters more for truth-value judgments: An investigation of quantifier scope ambiguity. Glossa: a journal of general linguistics.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.16995/glossa.5724

@article{scontras2021when,
  title = {{When pragmatics matters more for truth-value judgments: An investigation of quantifier scope ambiguity}},
  author = {Gregory Scontras and Lisa S. Pearl},
  journal = {Glossa: a journal of general linguistics},
  year = {2021},
  doi = {10.16995/glossa.5724},
  url = {https://doi.org/10.16995/glossa.5724}
}

References

39 references, 3 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Attali, Noa & Scontras, Gregory & Pearl, Lisa S. 2021. Pragmatic factors can explain variation in interpretation preferences for quantifier-negation utterances: A computational approach. In Proceedings of the 43rd annual meeting of the Cognitive Science Society, 917–923. DOI: https://doi.org/10.7275/3zax-6v78 ↗
  2. Bale, Alan & Barner, David. 2013. Grammatical alternatives and pragmatic development. In Fălăuş, Anamaria (ed.), Alternatives in semantics, 238–266. Palgrave Press. DOI: https://doi.org/10.1057/9781137317247 ↗
  3. Barner, David & Brooks, Neon & Bale, Alan. 2011. Accessing the unsaid: The role of scalar alternatives in children’s pragmatic inference. Cognition 118. 84–93. DOI: https://doi.org/10.1016/j.cognition.2010.10.010 ↗
  4. Breheny, Richard. 2008. A new look at the semantics and pragmatics of numerically quantified noun phrases. Journal of Semantics 25(2). 93–139. DOI: https://doi.org/10.1093/jos/ffm016 ↗
  5. Chemla, Emmanuel & Bott, Lewis. 2015. Using structural priming to study scopal represen tations and operations. Linguistic Inquiry 46. 157–172. DOI: https://doi.org/10.1162/LING_a_ 00178 ↗
  6. Conroy, Anastasia & Fults, Scott & Musolino, Julien & Lidz, Jeffrey. 2008. Surface scope as a default: The effect of time in resolving quantifier scope ambiguity. Poster presented at the 21st CUNY Conference on Sentence Processing. find
  7. Conroy, Anastasia Marie. 2008. The role of verification strategies in semantic ambiguity resolution in children and adults: University of Maryland, College Park dissertation. find
  8. Crain, Stephen & McKee, Cecile. 1985. The acquisition of structural restrictions on anaphora. In Proceedings of NELS, vol. 15. 94–110. find
  9. Crain, Stephen & Thornton, Rosalind. 1998. Investigations in Universal Grammar: A guide to research on the acquisition of syntax and semantics. Cambridge, MA: MIT Press. find
  10. Crain, Stephen & Thornton, Rosalind & Boster, Carole & Conway, Laura & Lillo-Martin, Diane & Woodams, Elaine. 1996. Quantification without qualification. Language Acquisition 5(2). 83–153. DOI: https://doi.org/10.1207/s15327817la0502_2 ↗
  11. Degen, Judith & Goodman, Noah D. 2014. Lost your marbles? The puzzle of dependent measures in experimental pragmatics. In Proceedings of the 36th annual meeting of the Cognitive Science Society, 397–402. find
  12. Denison, Stephanie & Reed, Christie & Xu, Fei. 2011. The emergence of probabilistic reasoning in very young infants. In Proceedings of the annual meeting of the Cognitive Science Society, vol. 33. 441–446. find
  13. Gerken, LouAnn. 2006. Decisions, decisions: Infant language learning when multiple generalizations are possible. Cognition 98(3). B67–B74. DOI: https://doi.org/10.1016/j.cognition. 2005.03.003 ↗
  14. Geurts, Bart. 2006. Take five: The meaning and use of a number word. In Vogeleer, Svetlana & Tasmowski, Liliane (eds.), Non-definiteness and plurality, 311–329. Amsterdam: Benjamins. DOI: https://doi.org/10.1075/la.95.16geu ↗
  15. Goodman, Noah D. & Frank, Michael C. 2016. Pragmatic language interpretation as probabilistic inference. Trends in Cognitive Sciences 20(11). 818–829. DOI: https://doi.org/10.1016/j. tics.2016.08.005 ↗
  16. Goodman, Noah D. & Stuhlmüller, Andreas. 2013. Knowledge and implicature: Modeling language understanding as social cognition. Topics in Cognitive Science 5(1). 173–184. DOI: https://doi.org/10.1111/tops.12007 ↗
  17. Gualmini, Andrea. 2004. Some knowledge children don’t lack. Linguistics, 957–982. DOI: https://doi.org/10.1515/ling.2004.034 ↗
  18. Gualmini, Andrea. 2008. The rise and fall of isomorphism. Lingua 118(8). 1158–1176. DOI: https://doi.org/10.1016/j.lingua.2008.02.003 ↗
  19. Gualmini, Andrea & Hulsey, Sarah & Hacquard, Valentine & Fox, Danny. 2008. The question-answer requirement for scope assignment. Natural Language Semantics 16(3). 205–237. DOI: https://doi.org/10.1007/s11050-008-9029-z ↗
  20. Jasbi, Masoud & Waldon, Brandon & Degen, Judith. 2019. Linking hypothesis and number of response options modulate inferred scalar implicature rate. Frontiers in Psychology 10. 189. DOI: https://doi.org/10.3389/fpsyg.2019.00189 ↗
  21. Kao, Justine T. & Bergen, Leon & Goodman, Noah D. 2014a. Formalizing the pragmatics of metaphor understanding. In Proceedings of the 36th annual meeting of the Cognitive Science Society, 719–724. find
  22. Kao, Justine T. & Wu, Jean Y & Bergen, Leon & Goodman, Noah D. 2014b. Nonliteral understanding of number words. Proceedings of the National Academy of Sciences 111(33). 12002–12007. DOI: https://doi.org/10.1073/pnas.1407479111 ↗
  23. Lassiter, D. & Goodman, N. D. 2013. Context, scale structure, and statistics in the interpretation of positive-form adjectives. In Semantics and Linguistic Theory (SALT) 23, 587–610. DOI: https://doi.org/10.3765/salt.v23i0.2658 ↗
  24. Musolino, Julien. 1998. Universal Grammar and the acquisition of semantic knowledge: An experimental investigation into the acquisition of quantifier-negation interaction in English: University of Maryland, College Park Doctoral dissertation. find
  25. Musolino, Julien. 2006. Structure and meaning in the acquisition of scope. In Semantics in acquisition, 141–166. Springer. DOI: https://doi.org/10.1007/1-4020-4485-2 ↗
  26. Musolino, Julien & Lidz, Jeffrey. 2003. The scope of isomorphism: Turning adults into children. Language Acquisition 11(4). 277–291. DOI: https://doi.org/10.1207/s15327817la1104_3 ↗
  27. Musolino, Julien & Lidz, Jeffrey. 2006. Why children aren’t universally successful with quantification. Linguistics 44. 817–852. DOI: https://doi.org/10.1515/LING.2006.026 ↗
  28. Pearl, Lisa. 2017. Evaluation, use, and refinement of knowledge representations through acquisition modeling. Language Acquisition 24(2). 126–147. DOI: https://doi.org/10.1080/104 89223.2016.1192633 Pearl, Lisa. In press. Modeling syntactic acquisition. In Sprouse, Jon (ed.), Oxford Handbook of Experimental Syntax, Oxford, UK: Oxford University Press. ↗
  29. Qing, Ciyang & Franke, Michael. 2015. Variations on a Bayesian theme: Comparing Bayesian models of referential reasoning. In Zeevat, H. & Schmitz, H.-C. (eds.), Bayesian natural language semantics and pragmatics, 201–220. Springer. DOI: https://doi.org/10.1007/978-3-319-17064-0 ↗
  30. Roberts, Craige. 2012. Information structure in discourse: Towards an integrated formal theory of pragmatics. Semantics and Pragmatics 5(6). 1–69. DOI: https://doi.org/10.3765/sp.5.6 ↗
  31. Savinelli, K. J. & Scontras, Gregory & Pearl, Lisa. 2017. Modeling scope ambiguity resolution as pragmatic inference: Formalizing differences in child and adult behavior. In Proceedings of the 39th Annual Meeting of the Cognitive Science Society, 3064–3069. find
  32. Savinelli, K. J. & Scontras, Gregory & Pearl, Lisa. 2018. Exactly two things to learn from modeling scope ambiguity resolution: Developmental continuity and numeral semantics. In Proceedings of the 8th Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2018), 67–75. DOI: https://doi.org/10.18653/v1/W18-0108 ↗
  33. Scontras, Gregory & Goodman, Noah D. 2017. Resolving uncertainty in plural predication. Cognition 168. 294–311. DOI: https://doi.org/10.1016/j.cognition.2017.07.002 Scontras, Gregory & Tessler, Michael Henry & Franke, Michael. electronic. Probabilistic language understanding: An introduction to the Rational Speech Act framework. Retrieved from https://www.problang.org. ↗
  34. Song, Yongjia & Jimenez, Abimael Hernandez & Scontras, Gregory. 2021. Cross-linguistic scope ambiguity: An investigation of English, Spanish, and Mandarin. Proceedings of the Linguistic Society of America 6. 572–586. DOI: https://doi.org/10.3765/plsa.v6i1.4992 ↗
  35. Tessler, Michael Henry & Goodman, Noah D. 2019. The language of generalization. Psychological Review 126. 395–436. DOI: https://doi.org/10.1037/rev0000142 ↗
  36. Thornton, Rosalind. 2017. The truth value judgment task: An update. In Nakayama, Mineharu & Su, Yi-Ching & Huang, Aijun (eds.), Studies in Chinese and Japanese language acquisition: In honor of Stephen Crain, 13–40. John Benjamins Publishing Company. DOI: https://doi.org/10.1075/ lald.60.02tho Viau, Joshua & Lidz, Jeffrey & Musolino, Julien. 2010. Priming of abstract logical representations in 4-year-olds. Language Acquisition 17(1–2). 26–50. DOI: https://doi.org/10.1080/1048922 1003620946 ↗
  37. Wason, Peter C. 1965. The contexts of plausible denial. Journal of Verbal Learning and Verbal Behavior 4(1). 7–11. DOI: https://doi.org/10.1016/S0022-5371(65)80060-3 ↗
  38. Xu, Fei & Tenenbaum, Joshua. 2007. Word Learning as Bayesian Inference. Psychological Review 114(2). 245–272. DOI: https://doi.org/10.1037/0033-295X.114.2.245 ↗
  39. Zaslavsky, Noga & Hu, Jennifer & Levy, Roger P. 2021. A Rate-Distortion view of human pragmatic reasoning. In Proceedings of the Society for Computation in Linguistics, vol. 4. DOI: https://doi.org/10.7275/gc1z-ck09 ↗

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