Documentation

Linglib.Phenomena.Quantification.Studies.ScontrasPearl2021

@cite{scontras-pearl-2021}: Quantifier Scope Ambiguity @cite{musolino-lidz-2003} #

"When pragmatics matters more for truth-value judgments: An investigation of quantifier scope ambiguity" Glossa 6(1): 110.

S&P is a modeling paper — it explains endorsement patterns from @cite{musolino-lidz-2003} and others via RSA, rather than reporting new experiments.

Part I: Truth Conditions & Shared Types #

Part II: Every-Not RSA Model (§3, EveryNot namespace) #

Domain: "Every horse didn't jump" with n=2 horses. 3 world states (0, 1, 2 jumped). 2 utterances (null, everyNot). 6 latent states (2 scopes × 3 QUDs).

Parameters: α = 1 (§3.2). P(w) = Binomial(n, b_suc).

QUDs (paper (3)) #

Three QUD partitions over worlds:

Compositional Grounding #

Truth conditions grounded in every_sem (Quantifier.lean), ScopeConfig/ScopeDerivation (Scope.lean).

Key Findings (Figure 2) #

S2 endorsement for "every horse didn't jump" in the partial world (w=1). The "Paper value" column is S&P's computed model predictions (not experimental data):

ConfigS2(everyNotw=1)
b_suc=0.1 (baseline)0.288~0.29
b_suc=0.5 (default)0.506~0.48 (read from Figure 2)
b_suc=0.9 (high base rate)0.796~0.80

QUD manipulation (Figure 2, center panel): favoring none? < how-many? < all? yields monotonically increasing endorsement (paper: 0.38, 0.48, 0.63).

Developmental Continuity (§3.3) #

Same model architecture explains child and adult behavior. Children's isomorphic (surface-scope) preference follows from low b_suc priors.

Part III: Two-Not RSA Model (§4, TwoNot namespace) #

Domain: "Two horses didn't jump" with n=4 horses. 5 world states (0–4 jumped). 2 utterances (null, twoNot). 10 latent states (2 scopes × 5 QUDs).

Central Claim (§4.2) #

Under exact semantics, surface scope pinpoints w=2 as the unique true world → high S2 endorsement (> 1/2). Under at-least semantics, surface scope is true at {w0,w1,w2} → low S2 endorsement (< 1/2). This predicts that adults endorse "two horses didn't jump" more readily in 2-of-4 contexts under exact numeral semantics.

How many horses jumped (out of 2).

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      Scope configuration for "every...not".

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          Truth conditions for "Every horse didn't jump" under each scope reading.

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              Numeral reading: does "two" mean exactly 2 or at least 2?

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                  Truth conditions for "two horses didn't jump" with n=4 horses (paper (6)).

                  Parameterized by numeral reading and scope configuration.

                  Surface scope (two > not): "There are two horses that didn't jump"

                  • Exact: exactly 2 didn't jump → exactly 2 jumped → w=2
                  • At-least: at least 2 didn't jump → at most 2 jumped → w ∈ {0,1,2}

                  Inverse scope (not > two): "It's not the case that two horses jumped"

                  • Exact: ¬(exactly 2 jumped) → w ≠ 2 → w ∈ {0,1,3,4}
                  • At-least: ¬(at least 2 jumped) → fewer than 2 jumped → w ∈ {0,1}
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                    For universals, surface scope (∀>¬: none jumped) ENTAILS inverse scope (¬>∀: not all jumped). If no horse jumped, then trivially not every horse jumped. This means no truth-value judgment context can diagnose the isomorphism effect for universals: whenever surface is true, inverse is automatically true too.

                    The entailment is strict: inverse does NOT entail surface. At w=1 (one horse jumped), inverse scope is true (not all jumped) but surface scope is false (not none jumped).

                    For exact numerals, surface scope does NOT entail inverse scope. At w=2 (exactly 2 jumped out of 4), surface is true (exactly 2 didn't) but inverse is false (it IS the case that exactly 2 jumped). This independence is what makes numerals diagnostic for the isomorphism effect.

                    Inverse scope also does not entail surface for exact numerals. At w=0 (none jumped), inverse is true (¬(exactly 2 jumped)) but surface is false (not exactly 2 didn't jump, since all 4 didn't).

                    Connects S&P's twoNotTruth truth conditions to linglib's numeral semantics infrastructure (maxMeaning in Numeral.Semantics).

                    The truth conditions in the data file are grounded in maxMeaning:

                    Convergent evidence for exact semantics from @cite{kennedy-2015} (de-Fregean semantics where bare numerals mean =n) and @cite{musolino-2004} (acquisition data — children reject "two" at w=3).

                    Exact surface: "exactly two didn't jump" (out of 4) ↔ exactly two jumped. Matches maxMeaning.eq 2 applied to the complement count (4 - w).

                    Utterances: null (silence) or "Every horse didn't jump".

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                        QUDs partition worlds by the question under discussion (paper (3)). Three QUD partitions for n=2 worlds.

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                            Flattened latent variable: scope reading × QUD. Flat inductive avoids Prod, keeping proof terms tractable for the kernel checker.

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                                RSA meaning derived from scopeTruth. Null utterance is always true (uninformative baseline).

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                                  2-horse domain for grounding truth conditions in quantifier semantics.

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                                    Jump predicate for each world state. In the 1-horse world, exactly h1 jumped (the choice is arbitrary; only cardinality matters for the universally quantified sentence).

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                                      Surface scope: ⟦every⟧(horse)(λx.¬jump(x))(w).

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                                        Inverse scope: ¬⟦every⟧(horse)(jump)(w).

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                                          "Every horse didn't jump" as a ScopeDerivation: a single syntactic form with multiple semantic values indexed by scope configuration.

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                                            RSA meaning is grounded in ScopeDerivation: the meaning function used by the RSA config matches the compositional scope derivation.

                                            The every-not scope pair has surface-entails-inverse structure: surface scope (none jumped) is a strict subset of inverse scope (not all jumped). This makes universals non-diagnostic for scope preferences — no TVJ context can distinguish isomorphic from non-isomorphic behavior.

                                            QUD answer function: q(w) → equivalence class identifier (paper (3)). For howMany, each world is its own class (identity partition).

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                                              Inline QUD projection: explicit case analysis, kernel-reducible. For howMany, each world is its own equivalence class (identity partition). For all?/none?, worlds sharing an answer are aggregated.

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                                                noncomputable def Phenomena.Quantification.Studies.ScontrasPearl2021.EveryNot.cfg (worldPr : JumpOutcome) (hwp : ∀ (w : JumpOutcome), 0 worldPr w) (scopePr : ScopeReading) (hsp : ∀ (s : ScopeReading), 0 scopePr s) (qudPr : QUD) (hqp : ∀ (q : QUD), 0 qudPr q) :

                                                @cite{scontras-pearl-2021} RSA model, parametric in three priors. S1 uses QUD-projected rpow with α = 1 (§3.2). L0 does not incorporate the world prior (footnote 6).

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                                                  World priors follow Binomial(2, b_suc), unnormalized: - b_suc = 0.1: P(w) ∝ (81, 18, 1) — horses unlikely to jump - b_suc = 0.5: P(w) ∝ (1, 2, 1) — symmetric - b_suc = 0.9: P(w) ∝ (1, 18, 81) — horses likely to jump

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                                                  Baseline: low base rate (b_suc = 0.1), uniform scope, uniform QUD. Best fit to adult Experiment 1 data (§3.2, Figure 2 left).

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                                                    @[reducible, inline]

                                                    Default: symmetric prior (b_suc = 0.5), uniform scope, uniform QUD. Binomial(2, 0.5) ∝ (1, 2, 1). Paper's default parameter setting.

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                                                      High base rate: b_suc = 0.9, uniform scope, uniform QUD. Tests robustness of S2 ordering to prior manipulation (Figure 2 left).

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                                                        Supportive context: b_suc = 0.9 + all?-biased QUD (1:18:1 ≈ 0.05:0.9:0.05). Models S&P's supportive-context prediction (§3.3, Figure 3), motivated by @cite{gualmini-etal-2008}'s finding that QUD manipulation increases endorsement. When both pragmatic factors are supportive, scope access has negligible impact on endorsement (paper: 0.91 at P(inv)=0.1 vs 0.91 at P(inv)=0.9).

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                                                          Surface-only: P(inverse) = 0. Tests whether scope ambiguity is needed to produce intermediate endorsement.

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                                                            QUD manipulation: favored QUD gets P = 0.9, others get P = 0.05. Default world prior (b_suc = 0.5) and default scope prior (uniform). Paper: "we see an increase in utterance endorsement from the none? (0.38) to how-many? (0.48) to all? (0.63) QUD."

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                                                            None?-biased QUD: P(none?) ≈ 0.9, P(howMany?) = P(all?) ≈ 0.05. Figure 2, center panel, leftmost bar (S2 ≈ 0.38).

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                                                              How-many?-biased QUD: P(howMany?) ≈ 0.9, P(all?) = P(none?) ≈ 0.05. Figure 2, center panel, middle bar (S2 ≈ 0.48).

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                                                                All?-biased QUD: P(all?) ≈ 0.9, P(howMany?) = P(none?) ≈ 0.05. Figure 2, center panel, rightmost bar (S2 ≈ 0.63).

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                                                                  S2 endorsement uses the generic RSAConfig.S2 from Theories/Pragmatics/RSA/Core/Config.lean: S2(u|w) = S2agent.policy(w, u) where S2agent.score(w, u) = cfg.L1(u, w) (the normalized L1 posterior).

                                                                  The `rsa_predict` tactic handles S2 cross-world goals via `policy_gt_cross`,
                                                                  building compositional QInterval proofs for the cross-product comparison. 
                                                                  

                                                                  Baseline L1: 0-jumped > 1-jumped. Both scopes agree w=0 is true; high prior weight (81 vs 18).

                                                                  Baseline L1: 1-jumped > 2-jumped. Inverse scope makes w=1 true; moderate prior advantage (18 vs 1).

                                                                  Scope ambiguity boosts partial-world endorsement. With both scopes active, L1(w=1) is higher than surface-only, because inverse scope directly makes w=1 true.

                                                                  Baseline S2: w0 > w1. The model predicts higher endorsement of "every horse didn't jump" when no horses jumped (none-scenario) than when one horse jumped (not-all scenario).

                                                                  S2 ordering robust to high base rate (b_suc = 0.9). Even when L1 reverses (w1 > w2 > w0 at L1), S2 still orders w0 > w1.

                                                                  QUD manipulation: how-many?-biased > none?-biased (Figure 2, center panel). Favoring the identity QUD yields higher endorsement than favoring the "did none succeed?" QUD, because how-many? makes the ambiguous utterance more informative at w=1 (partial success).

                                                                  QUD manipulation: all?-biased > how-many?-biased (Figure 2, center panel). Favoring the "did all succeed?" QUD yields the highest endorsement because both scope interpretations fully resolve all? at w=1 (the answer is "no" under either reading).

                                                                  Utterances: null (silence) or "two horses didn't jump".

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                                                                      QUDs for the two-not model (paper (7)). Five partitions over the 5-world domain. The two numeral-specific QUDs (two=?, two≥?) are added because explicitly mentioning a numeral makes that cardinality potentially relevant to the topic of conversation.

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                                                                          Flattened latent variable: scope reading × QUD. 2 scopes × 5 QUDs = 10 constructors.

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                                                                              Extract scope reading from latent variable.

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                                                                                Extract QUD from latent variable.

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                                                                                  RSA meaning for the two-not model, parameterized by numeral reading. Null utterance is always true (uninformative baseline).

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                                                                                    4-horse domain for grounding two-not truth conditions in numeral quantifier semantics.

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                                                                                      Exact surface scope: ⟦exactly 2⟧(horse)(λx.¬jump(x))(w). "There are exactly two horses that didn't jump."

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                                                                                        Exact inverse scope: ¬⟦exactly 2⟧(horse)(jump)(w). "It's not the case that exactly two horses jumped."

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                                                                                          Exact surface grounding: twoNotTruth .exact .surface derives from compositional ⟦exactly 2⟧(horse)(λx.¬jump(x)), not stipulation.

                                                                                          At-least surface scope: ⟦at least 2⟧(horse)(λx.¬jump(x))(w). "There are at least two horses that didn't jump."

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                                                                                            At-least inverse scope: ¬⟦at least 2⟧(horse)(jump)(w). "It's not the case that at least two horses jumped."

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                                                                                              The two grounding layers agree: maxMeaning .eq (count-based) and exactly_n_sem (GQT compositional) produce the same truth values. Chains twoNotExact_surface_matches_maxMeaning with exact_surface_from_exactly_n_sem by transitivity.

                                                                                              "Two horses didn't jump" as a ScopeDerivation under exact semantics: a single syntactic form with multiple semantic values indexed by scope.

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                                                                                                "Two horses didn't jump" as a ScopeDerivation under at-least semantics.

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                                                                                                  QUD projection for the 5-world domain (extends every-not QUDs; paper (7)). Explicit case analysis, kernel-reducible.

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                                                                                                    noncomputable def Phenomena.Quantification.Studies.ScontrasPearl2021.TwoNot.cfg (nr : NumeralReading) (worldPr : JumpOutcome4) (hwp : ∀ (w : JumpOutcome4), 0 worldPr w) (scopePr : ScopeReading) (hsp : ∀ (s : ScopeReading), 0 scopePr s) (qudPr : QUD5) (hqp : ∀ (q : QUD5), 0 qudPr q) :

                                                                                                    Two-not RSA model, parameterized by numeral reading and priors. Same architecture as the every-not model: S1 uses QUD-projected rpow with α = 1, L0 does not incorporate the world prior.

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                                                                                                      World priors follow Binomial(4, b_suc), unnormalized.

                                                                                                      The paper's central 2-of-4 predictions (Figure 7) use b_suc = 0.1
                                                                                                      with low P(inverse) = 0.1 (surface scope bias), matching the baseline
                                                                                                      parameters from the every-not model that produce low 1-of-2 endorsement.
                                                                                                      
                                                                                                      Binomial(4, 0.1) ∝ C(4,k) · 1^k · 9^(4-k) = (6561, 2916, 486, 36, 1). 
                                                                                                      
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                                                                                                      Baseline exact config: b_suc = 0.1, P(inv) = 0.1 (surface scope bias). Matches Figure 7 right panel, red bar (S2 ≈ 0.8).

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                                                                                                        Baseline at-least config: same parameters, at-least numeral semantics. Matches Figure 7 left panel, red bar (S2 ≈ 0.1).

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                                                                                                          The paper's central claims for the 2-of-4 context (Figure 7).

                                                                                                          Under exact semantics with low base rate (b_suc = 0.1) and surface scope
                                                                                                          bias (P(inv) = 0.1), surface scope pinpoints w=2 as the unique true world,
                                                                                                          giving maximum informativity → high S2 endorsement at w=2.
                                                                                                          
                                                                                                          Under at-least semantics with the same parameters, surface scope is true
                                                                                                          at {w0,w1,w2}, diluting informativity → low S2 endorsement at w=2.
                                                                                                          
                                                                                                          The 1-of-2 vs 2-of-4 asymmetry: these SAME "baseline" parameters produce
                                                                                                          low endorsement (27.5%) in the 1-of-2 context but high endorsement in the
                                                                                                          2-of-4 context, but ONLY under exact numeral semantics. This is the
                                                                                                          paper's key argument for exact semantics as the basic numeral meaning.
                                                                                                          
                                                                                                          The `rsa_predict` tactic handles the S2 computation via reflection,
                                                                                                          building L0→S1→L1→S2 layers and comparing exact rational bounds. 
                                                                                                          

                                                                                                          Under exact semantics with baseline parameters (b_suc=0.1, P(inv)=0.1), S2 endorsement of "two horses didn't jump" at w=2 exceeds 1/2. Surface scope pinpoints w=2 as the unique true world, giving maximum informativity (Figure 7 right, red bar ≈ 0.8).

                                                                                                          Under at-least semantics with baseline parameters, S2 endorsement at w=2 is below 1/2 (Figure 7 left, red bar ≈ 0.1). Surface scope is true at {w0,w1,w2}, diluting informativity.

                                                                                                          Under at-least semantics with baseline parameters, S2 endorsement at w=2 is lower than under exact semantics. Exact surface has 1 true world; at-least surface has 3. (Figure 7: right panel > left panel at matching P(inv).)

                                                                                                          The key informativity contrast: under exact semantics, surface scope has exactly 1 true world (w2), while under at-least it has 3 (w0–w2). This drives the endorsement difference via S1 informativity.

                                                                                                          Exact inverse has 4 true worlds (w0,w1,w3,w4) — very uninformative. Since w2 is the only world where surface scope is true, inverse scope contributes nothing at w2 (it's false there), explaining why surface scope dominates the S2 prediction under exact semantics.

                                                                                                          At-least inverse has 2 true worlds (w0,w1) — more informative than exact inverse's 4, but still less informative than exact surface's 1.

                                                                                                          The paper's key argument: the SAME "baseline" parameters that produce low 1-of-2 endorsement also produce high 2-of-4 endorsement — but only under exact numeral semantics.

                                                                                                          The models have different world types (JumpOutcome vs JumpOutcome4),
                                                                                                          so we state this as two separate bounds that together establish the
                                                                                                          1-of-2 vs 2-of-4 asymmetry:
                                                                                                          
                                                                                                          - Every-not baseline: S2(everyNot|w=1) < 1/2 (low)
                                                                                                          - Two-not exact baseline: S2(twoNot|w=2) > 1/2 (high)
                                                                                                          - Two-not at-least baseline: S2(twoNot|w=2) < 1/2 (low)
                                                                                                          
                                                                                                          The first two use "baseline" parameters (b_suc=0.1, P(inv)=0.1).
                                                                                                          The asymmetry between the second and third is the argument for exact
                                                                                                          semantics: changing only the numeral reading flips the prediction. 
                                                                                                          

                                                                                                          Every-not baseline endorsement at w=1 is below 1/2. This is the low-endorsement end of the 1-of-2 vs 2-of-4 asymmetry. Uses the same b_suc=0.1 parameter that the TwoNot baseline uses.

                                                                                                          Cross-model summary (proved above): - everyNot_baseline_endorsement_low: S2(everyNot|w=1) < 1/2 - TwoNot.exact_baseline_endorsement_high: S2(twoNot|w=2) > 1/2 - TwoNot.atleast_baseline_endorsement_low: S2(twoNot|w=2) < 1/2

                                                                                                          Same parameters, different domain size, same model architecture.
                                                                                                          The exact/at-least split is the only difference between high and low
                                                                                                          endorsement in the 2-of-4 context.