Scontras and Pearl (2021): When Pragmatics Matters More for Truth-Value Judgments #
Scontras and Pearl model the truth-value judgment task for the scopally ambiguous sentences
every horse didn't jump and two horses didn't jump. A world is the number of horses that
jumped. A speaker who knows the world, a scope interpretation and a question under discussion
chooses between the sentence and silence, aiming to convey the answer to the question (S1). A
pragmatic listener inverts that speaker jointly over worlds, interpretations and questions
(L1). The endorsing speaker, who sees only the world, chooses between the sentence and silence
by the listener's beliefs about the world (S2), so that a truth-value judgment is a production
decision.
Main results #
one_half_lt_S2_real_amb_iff: the sentence is endorsed more often than not exactly when it is produced at the observed world more often than on average.S2_real_amb_all: once the question is whether all the horses jumped, the two interpretations of every horse didn't jump produce alike and the scope prior does not affect endorsement.S2_real_amb_mono_baseRate: production falls with the number of jumpers, so a higher success base rate in the binomial world prior raises endorsement.share_twoAtLeast_le_share_twoExact,share_twoExact_lt_share_twoAtLeast: with four horses and two jumpers the surface speaker favors the exact reading whatever the question, while the inverse speaker asked whether all jumped favors the at-least reading.
Implementation notes #
- The rationality of
S1and the priors on worlds, interpretations and questions are parameters. - A determiner is a quantifier on van Benthem's tree of numbers, a relation between the number
of restrictor members outside its scope and the number inside it. Every is
NumberTree.all, a numeral is the cardinal quantifier of the interval of aDegree.Comparison, and a scope interpretation is a negation of the determiner (Scope.negation), inner for surface scope and outer for inverse scope. The paper tabulates the truth conditions at two and at four horses, and here they follow for any number. - Both models share the paper's five questions. The every-not model is the case of a question prior carried by the first three.
- Utterances are costless and the literal listener carries no world prior, following Qing and Franke. The speaker addresses the question as in the hyperbole model of Kao and colleagues.
TODO #
- The numerical predictions of the paper's figures, at unit rationality and its grid of priors, are not stated.
- The base-rate result is proved at two horses, the paper's scenario. For any number of horses
it would follow from
productionbeing antitone and the binomial family being stochastically increasing in its success probability.
References #
The utterances of (2) are the null utterance, which says nothing, and the scopally ambiguous test sentence.
Instances For
Equations
- ScontrasPearl2021.instDecidableEqUtt x✝ y✝ = if h : x✝.ctorIdx = y✝.ctorIdx then isTrue ⋯ else isFalse ⋯
Equations
- ScontrasPearl2021.instFintypeUtt = { elems := { val := ↑ScontrasPearl2021.Utt.enumList, nodup := ScontrasPearl2021.Utt.enumList_nodup }, complete := ScontrasPearl2021.instFintypeUtt._proof_1 }
Equations
The scope interpretations of the test sentence put the determiner over negation, or negation over the determiner.
Instances For
Equations
- ScontrasPearl2021.instDecidableEqScope x✝ y✝ = if h : x✝.ctorIdx = y✝.ctorIdx then isTrue ⋯ else isFalse ⋯
Equations
- One or more equations did not get rendered due to their size.
Equations
Equations
- ScontrasPearl2021.instDecidableEqQUD x✝ y✝ = if h : x✝.ctorIdx = y✝.ctorIdx then isTrue ⋯ else isFalse ⋯
Equations
- ScontrasPearl2021.instFintypeQUD = { elems := { val := ↑ScontrasPearl2021.QUD.enumList, nodup := ScontrasPearl2021.QUD.enumList_nodup }, complete := ScontrasPearl2021.instFintypeQUD._proof_1 }
Equations
A world is the number of horses, out of n, that jumped.
Equations
- ScontrasPearl2021.World n = Fin (n + 1)
Instances For
Semantics #
A scope interpretation negates the determiner on the tree of numbers. The surface interpretation, the determiner over negation, is its inner negation, and the inverse interpretation, negation over the determiner, is its outer negation.
Equations
Instances For
On its surface interpretation every horse didn't jump is no horse jumped, the paper's "none" reading.
On its inverse interpretation every horse didn't jump is not all horses jumped, the paper's "not-all" reading.
A numeral is the cardinal quantifier of the interval that the comparison c selects at k.
The comparison .eq gives the exact reading and .ge the at-least reading.
Equations
Instances For
The numeral two on its exact reading.
Instances For
The numeral two on its at-least reading.
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The extension of an utterance under a scope interpretation ((2), (6)) among n horses. The
null utterance is true everywhere. With w jumpers, n - w horses lie outside the scope jump
and w inside it, and D horses didn't jump is true when the interpretation's negation of D
holds of those two counts.
Equations
- ScontrasPearl2021.ext D n x✝ ScontrasPearl2021.Utt.null = Finset.univ
- ScontrasPearl2021.ext D n x✝ ScontrasPearl2021.Utt.amb = (x✝.negation D).row n
Instances For
On its surface interpretation every horse didn't jump is true where none jumped (2).
On its inverse interpretation every horse didn't jump is true where not all jumped (2).
On its surface interpretation a numeral sentence is true where the number of non-jumpers stands in the numeral's comparison to its value (6).
On its inverse interpretation a numeral sentence is true where the number of jumpers does not stand in the numeral's comparison to its value (6).
The extension sees a determiner only at counts that sum to the number of horses, a row of the tree of numbers, so determiners that agree there have the same extensions.
Among n restrictor members, exactly n lie inside the scope just in case none lies
outside.
Among n restrictor members, at least n lie inside the scope just in case none lies
outside.
With as many horses as the numeral counts, the numeral sentence on the exact reading is true exactly where every horse didn't jump is, on both interpretations (§4.2.1).
With as many horses as the numeral counts, the numeral sentence on the at-least reading is true exactly where every horse didn't jump is, on both interpretations (§4.2.1).
With four horses, two horses didn't jump on the exact reading of the numeral is true on its surface interpretation only where exactly two jumped ((6), Figure 5).
With four horses, two horses didn't jump on the exact reading is true on its inverse interpretation wherever the number of jumpers is not two (6).
With four horses, two horses didn't jump on the at-least reading is true on its surface interpretation where fewer than three jumped (6).
With four horses, two horses didn't jump on the at-least reading is true on its inverse interpretation where fewer than two jumped (6).
The projection of a question ((3), (7)) sends a world to its answer, the number of jumpers for how many?, and for the polar questions whether all, none, exactly two or at least two jumped.
Equations
- ScontrasPearl2021.project n ScontrasPearl2021.QUD.howMany x✝ = Sum.inl x✝
- ScontrasPearl2021.project n ScontrasPearl2021.QUD.all x✝ = Sum.inr (decide (x✝ = Fin.last n))
- ScontrasPearl2021.project n ScontrasPearl2021.QUD.none x✝ = Sum.inr (decide (x✝ = 0))
- ScontrasPearl2021.project n ScontrasPearl2021.QUD.exactlyTwo x✝ = Sum.inr (decide (↑x✝ = 2))
- ScontrasPearl2021.project n ScontrasPearl2021.QUD.atLeastTwo x✝ = Sum.inr (decide (2 ≤ ↑x✝))
Instances For
The cell of a world under a question is the set of worlds sharing its answer.
Equations
- ScontrasPearl2021.cell n q w = {w' : ScontrasPearl2021.World n | ScontrasPearl2021.project n q w' = ScontrasPearl2021.project n q w}
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With two horses, exactly two? asks whether all jumped.
With two horses, at least two? asks whether all jumped.
The model #
The literal listener is uniform on the extension of the utterance under the scope interpretation, with no world prior (fn. 6).
Equations
- ScontrasPearl2021.L0 D n i = RSA.listener (ProbabilityTheory.uniformOn Set.univ) (RSA.truthful fun (u : ScontrasPearl2021.Utt) => ↑(ScontrasPearl2021.ext D n i u))
Instances For
The literal listener of a scope interpretation gives a world's answer to a question the fraction of the extension lying in the world's cell.
The speaker of §3.1, at a world, a scope interpretation and a question, knows the world's
answer to the question and is informative at rationality α about the belief of the
interpretation's literal listener about it. The utterances are costless (fn. 8).
Equations
- One or more equations did not get rendered due to their size.
Instances For
The pragmatic listener of §3.1 inverts the speaker over worlds, interpretations and
questions, against the product of the world prior and a prior on interpretations and questions
(the paper's P(i) P(q)).
Equations
- ScontrasPearl2021.L1 D n α μ ν = RSA.listener (μ.prod ν) ⇑(ScontrasPearl2021.S1 D n α)
Instances For
The endorsing speaker of §3.1 is, at the observed world, the best response at unit rationality to the world marginal of the pragmatic listener.
Equations
- ScontrasPearl2021.S2 D n α μ ν = RSA.speaker 1 0 (⇑(ScontrasPearl2021.L1 D n α μ ν).fst) MeasureTheory.Measure.dirac
Instances For
With as many horses as the numeral counts, the numeral model on the exact reading is the every-not model (§4.2.1).
With as many horses as the numeral counts, the numeral model on the at-least reading is the every-not model (§4.2.1).
Production shares #
The cell mass is the fraction of the extension of an utterance under a scope interpretation that lies in the cell of a world under a question, which is the literal listener's mass on the world's answer.
Equations
- ScontrasPearl2021.cellMass D n l u w = (((ScontrasPearl2021.L0 D n l.1).map (ScontrasPearl2021.project n l.2)) u).real {ScontrasPearl2021.project n l.2 w}
Instances For
The null utterance takes the rest of the speaker's mass.
The cell count is the number of worlds in the cell of w under the question of l at which
u is true under the interpretation of l.
Equations
- ScontrasPearl2021.cellCount D n l u w = (ScontrasPearl2021.ext D n l.1 u ∩ ScontrasPearl2021.cell n l.2 w).card
Instances For
Endorsement #
The production at a world is the probability that the test sentence is produced there, the
interpretation and the question drawn from ν.
Equations
- ScontrasPearl2021.production D n α ν w = ∑ l : ScontrasPearl2021.Scope × ScontrasPearl2021.QUD, ν.real {l} * ScontrasPearl2021.share D n α l w
Instances For
The expected production is the prior probability that the test sentence is produced, the observation marginal of the speaker.
Equations
- ScontrasPearl2021.expectedProduction D n α μ ν = ((μ.prod ν).bind ⇑(ScontrasPearl2021.S1 D n α)).real {ScontrasPearl2021.Utt.amb}
Instances For
The test sentence is produced with positive probability at a world at which it is true on both interpretations.
endorse m z is the endorsement probability as a function of the production probability m
at the observed world and its expectation z under the world prior.
Equations
- ScontrasPearl2021.endorse m z = m * (1 - z) / (m * (1 - z) + z * (1 - m))
Instances For
Endorsement rises with the production probability at the observed world.
Endorsement falls with the expected production probability.
The sentence is endorsed more often than not exactly when it is produced at the observed world more often than on average.
The endorsement probability at a world of positive prior is the production probability at the world played against its expectation under the world prior (§3.1).
The sentence is endorsed more often than not exactly when it is produced at the observed world more often than on average under the world prior (§5.1).
The every-not model (§3) #
The sentence every horse didn't jump is true at the no-success world on both interpretations.
Under the question all? the sentence every horse didn't jump fully resolves the question in the negative on either interpretation, as its literal listener is certain of the answer at every world short of total success.
Under the question all? the two scope interpretations of every horse didn't jump produce alike at every world, since the sentence answers the question in the negative on either reading (§3.2, §5.1).
With the question settled as all?, the production of the sentence at a world does not depend on the scope prior.
With the question settled as all?, endorsement does not depend on the scope prior, which matters only through questions the two interpretations answer differently (§3.2, Figure 3).
Two horses (§3.2) #
Raising the success base rate of the binomial world prior raises endorsement at every world (§3.2, Figure 2), since the more success is expected, the more the sentence rules out on either reading.