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Presuppositions, provisos, and probability

Paper · 2012 · Daniel Lassiter

Summary

The paper addresses the proviso problem identified by Geurts (1996). Satisfaction theories in the Heim tradition predict that when a conditional's consequent carries a presupposition, it projects only weakly, as "if p then q". Yet speakers often infer plain q, as with Theo's manager, and only sometimes the conditional, as with John's wetsuit. Lassiter keeps the dynamic, satisfaction-based architecture, building on Klinedinst & Rothschild (2012) and Yalcin (2007). He changes one thing: information states are probability distributions rather than sets of worlds. A presupposition then becomes a requirement that some content be highly probable in the participants' epistemic state. For conditionals, this yields constraints on conditional probabilities. The theory picks the best English rendering of those constraints from what listeners know about dependencies between antecedent and presupposition. As a result, unconditional readings follow without any extra strengthening mechanism. The account also handles semi-conditional presuppositions and gives a graded measure of how hard accommodation is. This matters because it removes a central objection to satisfaction theories using machinery motivated independently by work on modality and cognition.

Key points

  • Core contrast: for a conditional "if p, …q…", the plain-q paraphrase wins when q is probabilistically independent of p. The conditional paraphrase wins when pr(q|p) exceeds pr(q).
  • New data: unconditional inferences also appear when pr(q|p) is lower than pr(q). This undercuts strengthening accounts based on irrelevance or independence alone, such as Schlenker (2011) and Singh (2007).
  • The account explains semi-conditional presuppositions. In the "diver and wants to impress his girlfriend" case, only the diver part conditions the wetsuit inference.
  • It also explains why "Sam knows that if p then q" presupposes the conditional itself and does not get strengthened.
  • Accommodation difficulty is modelled as surprisal, I(φ) = −log₂ pr(φ). This cost is zero for certain content, infinite for impossible content, and rises as probability falls. Local accommodation (e.g. "I don't have a pet ferret") is left open as an unsolved challenge.

Interesting ideas

  • I1. Rather than adding a special rule that turns a weak conditional inference into a stronger one, the paper lets the stronger reading fall out of the probabilistic structure, which avoids stipulated patches.
  • I2. Whether an inference is stated conditionally or unconditionally depends on how the antecedent shifts the probability of the presupposed content: raised, unchanged or lowered.
  • I3. Surprisal gives a single graded, explainable cost for accepting unstated background material, in place of a yes/no felicity check.
  • I4. The paper argues by contrast pairs: minimally different sentences whose inferences diverge, as in the wetsuit, manager and "Sam knows" cases.

For Mova

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

  • I1 (frames): In crate prag, presupposition projection could be computed once, with the global-versus-conditional choice made by consulting dependency knowledge, instead of keeping a list of strengthening exceptions.
  • I2 (world): The world model could store coarse dependency labels between states (e.g. "being a diver raises owning a wetsuit") so that the system can explain why a presupposed entity, such as Theo's manager, is added globally.
  • I3 (answer): When a QA reader must accommodate a presupposition from a fable question, it could log the −log₂ probability as a confidence cost and flag answers that depend on highly surprising accommodations.
  • I4 (learn): These minimal pairs could become a negative-control test suite, checking that the presupposition module projects globally in one case and conditionally in its twin.

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

🎓Stanford UniversityStanford · United States310 works · 284,480 citations🎓New York UniversityNew York · United States136 works · 86,390 citations

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 9,086 worksArtificial Intelligence · 8,889 worksArts and Humanities · 9,296 worksLanguage and Linguistics · 9,206 worksPhilosophy · 211 works

🧭 Topics 1Research subjects and languages the work is about.Implicature · 72 works

Cite

Cite as Daniel Lassiter. 2012. Presuppositions, provisos, and probability. Semantics and Pragmatics.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.3765/sp.5.2

@article{lassiter2012presuppositions,
  title = {{Presuppositions, provisos, and probability}},
  author = {Daniel Lassiter},
  journal = {Semantics and Pragmatics},
  year = {2012},
  doi = {10.3765/sp.5.2},
  url = {https://doi.org/10.3765/sp.5.2}
}

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