@cite{goodman-stuhlmuller-2013}: Empirical Data #
@cite{goodman-stuhlmuller-2013}
"Knowledge and Implicature: Modeling Language Understanding as Social Cognition" Topics in Cognitive Science 5(1): 173-184
Paradigm #
Three objects that may have a property. Speaker observes a subset (access = 1, 2, or 3) and makes a quantified or numeral statement. Listener divides $100 among world states (0-3 objects have property). Speaker access is common knowledge.
Trials with knowledgeability bet <= 70 excluded from primary analysis.
Qualitative Findings #
The paper's central finding: scalar implicature and upper-bounded numeral interpretations are modulated by speaker knowledge. When the speaker has full access, listeners draw upper-bounded inferences; when access is partial, these inferences weaken or disappear.
| # | Finding | Word | Access | Comparison | Evidence |
|---|---|---|---|---|---|
| 1 | Implicature present | "some" | 3 | state 2 > state 3 | t(43)=-10.2, p<.001 |
| 2 | Implicature canceled | "some" | 1 | state 2 not > state 3 | t(31)=0.77, p=.78 |
| 3 | Implicature canceled | "some" | 2 | state 2 not > state 3 | t(28)=-0.82, p=.21 |
| 4 | Upper-bounded | "two" | 3 | state 2 > state 3 | t(43)=-10.2, p<.001 |
| 5 | Not upper-bounded | "two" | 2 | state 2 not > state 3 | t(24)=1.1, p=.87 |
| 6 | Upper-bounded | "one" | 3 | state 1 > state 2 | t(42)=-13.1, p<.001 |
| 7 | Upper-bounded | "one" | 3 | state 1 > state 3 | t(42)=-17.1, p<.001 |
| 8 | Not upper-bounded | "one" | 1 | state 1 not > state 2 | t(24)=1.9, p=.96 |
| 9 | Not upper-bounded | "one" | 1 | state 1 not > state 3 | t(24)=3.2, p=1.0 |
| 10 | Partial | "one" | 2 | state 1 > state 3 | t(25)=-3.9, p<.001 |
| 11 | Partial | "one" | 2 | state 1 not > state 2 | t(25)=1.5, p=.92 |
The 11 qualitative findings from @cite{goodman-stuhlmuller-2013} Experiments 1-2. Each finding is a pairwise bet comparison between world states under a specific (word, access) condition.
- some_full_implicature : Finding
Full access: bets on state 2 > state 3 (scalar implicature present). Evidence: t(43) = -10.2, p < .001.
- some_minimal_canceled : Finding
Minimal access (a=1): state 2 does not exceed state 3 (canceled). Evidence: t(31) = 0.77, p = .78.
- some_partial_canceled : Finding
Partial access (a=2): state 2 does not exceed state 3 (canceled). Evidence: t(28) = -0.82, p = .21.
- two_full_upper_bounded : Finding
Full access: "two" -> state 2 > state 3 (upper-bounded reading). Evidence: t(43) = -10.2, p < .001.
- two_partial_weakened : Finding
Partial access (a=2): state 2 does not exceed state 3 (weakened). Evidence: t(24) = 1.1, p = .87.
- one_full_1v2 : Finding
Full access: "one" -> state 1 > state 2. Evidence: t(42) = -13.1, p < .001.
- one_full_1v3 : Finding
Full access: "one" -> state 1 > state 3. Evidence: t(42) = -17.1, p < .001.
- one_minimal_1v2_canceled : Finding
Minimal access (a=1): state 1 does not exceed state 2 (canceled). Evidence: t(24) = 1.9, p = .96.
- one_minimal_1v3_canceled : Finding
Minimal access (a=1): state 1 does not exceed state 3 (canceled). Evidence: t(24) = 3.2, p = 1.0.
- one_partial_1v3 : Finding
Partial access (a=2): state 1 > state 3 (partial implicature holds). Evidence: t(25) = -3.9, p < .001.
- one_partial_1v2_canceled : Finding
Partial access (a=2): state 1 does not exceed state 2 (still canceled). Evidence: t(25) = 1.5, p = .92.
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All findings from the paper.
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A pairwise comparison of bets on two world states in a condition.
The key observable: did participants allocate significantly more money to
world state stateA than to stateB? A theory that predicts the listener's
posterior P(state | word, access) can be checked against this.
- experiment : ℕ
- word : String
- access : ℕ
How many of 3 objects the speaker observed
- stateA : ℕ
- stateB : ℕ
- aExceedsB : Bool
Did bets on stateA significantly exceed bets on stateB?
- evidence : String
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Access = 3: bets on state 2 > bets on state 3.
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Access = 1: bets on state 2 did NOT exceed bets on state 3.
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Access = 2: bets on state 2 did NOT exceed bets on state 3.
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Bets on state 3 at access = 3 significantly lower than at access = 1.
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"two", access = 3: bets on state 2 > bets on state 3.
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"two", access = 2: bets on state 2 did NOT exceed bets on state 3.
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"one", access = 3: bets on state 1 > bets on state 2.
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"one", access = 3: bets on state 1 > bets on state 3.
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"one", access = 1: bets on state 1 did NOT exceed bets on state 2.
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"one", access = 1: bets on state 1 did NOT exceed bets on state 3.
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"one", access = 2: bets on state 1 > bets on state 3 (partial).
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"one", access = 2: bets on state 1 did NOT exceed bets on state 2.
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- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.exp2_access_main = { description := "Main effect of access", testType := "ANOVA, F(2, 205)", statistic := 6.57, p := 1e-2 }
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- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.exp2_word_main = { description := "Main effect of word", testType := "ANOVA, F(2, 205)", statistic := 269.8, p := 1e-3 }
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- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.exp2_interaction = { description := "Word x access interaction", testType := "ANOVA, F(1, 205)", statistic := 34.7, p := 1e-3 }
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- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.knowledge_a1 = { access := 1, meanBet := 27.1, sd := 4.9 }
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- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.knowledge_a2 = { access := 2, meanBet := 34.8, sd := 5.7 }
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- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.knowledge_a3 = { access := 3, meanBet := 93.0, sd := 2.7 }
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The statistical evidence for each finding.
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- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Finding.some_full_implicature.evidence = Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.exp1_some_a3_2v3
- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Finding.some_minimal_canceled.evidence = Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.exp1_some_a1_2v3
- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Finding.some_partial_canceled.evidence = Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.exp1_some_a2_2v3
- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Finding.two_full_upper_bounded.evidence = Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.exp2_two_a3_2v3
- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Finding.two_partial_weakened.evidence = Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.exp2_two_a2_2v3
- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Finding.one_full_1v2.evidence = Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.exp2_one_a3_1v2
- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Finding.one_full_1v3.evidence = Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.exp2_one_a3_1v3
- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Finding.one_minimal_1v2_canceled.evidence = Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.exp2_one_a1_1v2
- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Finding.one_minimal_1v3_canceled.evidence = Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.exp2_one_a1_1v3
- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Finding.one_partial_1v3.evidence = Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.exp2_one_a2_1v3
- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Finding.one_partial_1v2_canceled.evidence = Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.exp2_one_a2_1v2
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Does this finding predict that the comparison holds (stateA > stateB)?
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- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Finding.some_full_implicature.predicted = true
- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Finding.two_full_upper_bounded.predicted = true
- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Finding.one_full_1v2.predicted = true
- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Finding.one_full_1v3.predicted = true
- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Finding.one_partial_1v3.predicted = true
- x✝.predicted = false
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Bridge content (merged from Bridge.lean) #
@cite{goodman-stuhlmuller-2013}: RSA Bridge #
@cite{goodman-stuhlmuller-2013}
The paper's RSA model applied to the experimental data. A single gsCfg
constructor parametric in the meaning function serves both experiments
(quantifiers and lower-bound numerals). They share the same observation
model, speaker belief, and S1 structure — the only thing that varies is
the utterance type and literal semantics.
Architecture (Eq. 1–5) #
F_w : s → {0, 1} truth function for utterance w (§1)
P_lex(s | w) ∝ δ_{F_w(s)} literal interpretation (§1)
U(w; s) = ln P_lex(s | w) informativity (negative surprisal) (Eq. 3)
P(s | o, a) speaker's belief state (hypergeometric)
S1(w | o, a) ∝ exp(α · E_{P(s|o,a)}[U(w; s)]) (Eq. 2)
= exp(α · Σ_s P(s | o, a) · ln P_lex(s | w))
S1(w | s, a) = Σ_o S1(w | o, a) · P(o | a, s) (Eq. 4)
L1(s | w, a) ∝ S1(w | s, a) · P(s) (Eq. 1)
The speaker observes a subset of objects (hypergeometric sampling), forms a belief P(s | o, a), and soft-max optimizes expected informativity under that belief. L1 marginalizes over observations weighted by P(o | a, s).
The quality filter (utterance must be true at all worlds the speaker
considers possible) is explicit because Real.log 0 = 0 in Lean/Mathlib,
unlike WebPPL where log(0) = -∞ makes quality emerge from the score.
The model reproduces all 11 findings. All proofs use rsa_predict.
Data source: GoodmanStuhlmuller2013.Data
Theories used: Theories.Pragmatics.RSA.Core.Config,
Theories.Pragmatics.RSA.Quantities,
Theories.Semantics.Lexical.Numeral.Semantics
World states: how many of 3 objects have the property.
- s0 : WorldState
- s1 : WorldState
- s2 : WorldState
- s3 : WorldState
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- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.WorldState.s0.toNat = 0
- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.WorldState.s1.toNat = 1
- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.WorldState.s2.toNat = 2
- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.WorldState.s3.toNat = 3
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- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Obs.o0a1.access = Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Access.a1
- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Obs.o1a1.access = Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Access.a1
- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Obs.o0a2.access = Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Access.a2
- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Obs.o1a2.access = Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Access.a2
- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Obs.o2a2.access = Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Access.a2
- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Obs.o0a3.access = Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Access.a3
- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Obs.o1a3.access = Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Access.a3
- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Obs.o2a3.access = Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Access.a3
- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Obs.o3a3.access = Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Access.a3
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Number of objects with the property in the sample.
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- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Obs.o0a1.count = 0
- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Obs.o0a2.count = 0
- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Obs.o0a3.count = 0
- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Obs.o1a1.count = 1
- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Obs.o1a2.count = 1
- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Obs.o1a3.count = 1
- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Obs.o2a2.count = 2
- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Obs.o2a3.count = 2
- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Obs.o3a3.count = 3
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Sample size (= access level as ℕ).
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- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Obs.o0a1.sampleSize = 1
- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Obs.o1a1.sampleSize = 1
- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Obs.o0a2.sampleSize = 2
- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Obs.o1a2.sampleSize = 2
- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Obs.o2a2.sampleSize = 2
- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Obs.o0a3.sampleSize = 3
- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Obs.o1a3.sampleSize = 3
- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Obs.o2a3.sampleSize = 3
- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.Obs.o3a3.sampleSize = 3
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Hypergeometric feasibility: can you draw obs.count successes when
sampling obs.sampleSize from a population of 3 with s.toNat successes?
True iff C(K, k) > 0 and C(3−K, n−k) > 0, i.e. k ≤ K and n−k ≤ 3−K.
Derived from the combinatorial constraint rather than stipulated.
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P(obs | access, world). Hypergeometric probability of observing k successes
when sampling n from 3 total with K successes:
P(k | N=3, K, n) = C(K,k) · C(3−K, n−k) / C(3,n).
Instantiates Core.Distributions.hypergeometric for N=3.
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The observation model is an instance of the general hypergeometric
from Core.Distributions (N=3). The ℝ-arithmetic form is used
for rsa_predict compatibility; this theorem witnesses the equivalence.
Speaker's posterior over world states given observation: P(state | obs) ∝ P(obs | state, access) · P(state). With uniform world prior, this is the normalized hypergeometric. The access level is derived from the observation itself.
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Quality filter: utterance u must be true at every world the speaker
considers possible given observation obs. Explicit because Real.log 0 = 0
in Lean; in WebPPL, log(0) = -∞ makes quality emerge from the score.
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GS2013 model parametric in utterance type and meaning function.
Eq. 1–4 from the paper: P_lex(s | w) ∝ ⟦w⟧(s) (literal listener) U(w; s) = ln P_lex(s | w) (Eq. 3) S1(w | o, a) ∝ exp(α · Σ_s P(s | o, a) · U(w; s)) (Eq. 2) S1(w | s, a) = Σ_o S1(w | o, a) · P(o | a, s) (Eq. 4) L1(s | w, a) ∝ S1(w | s, a) · P(s) (Eq. 1)
The quality filter ensures the speaker only considers utterances true at
all worlds compatible with their observation. L1 marginalizes over
observations via latentPrior w obs = P(obs | a, w).
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- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.qMeaning Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.QUtt.none_ x✝ = (x✝.toNat == 0)
- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.qMeaning Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.QUtt.some_ x✝ = decide (x✝.toNat ≥ 1)
- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.qMeaning Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.QUtt.all x✝ = (x✝.toNat == 3)
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Quantifier RSA model: {none, some, all} × speaker access.
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- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.lbMeaning Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.NumUtt.one x✝ = decide (x✝.toNat ≥ 1)
- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.lbMeaning Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.NumUtt.two x✝ = decide (x✝.toNat ≥ 2)
- Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.lbMeaning Phenomena.ScalarImplicatures.GoodmanStuhlmuller2013.NumUtt.three x✝ = decide (x✝.toNat ≥ 3)
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Lower-bound numeral RSA model: {one, two, three} × speaker access.
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Quantifier meaning derives from Montague semantics (not stipulated).
Lower-bound numeral meaning derives from NumeralTheory.meaning.
Full access: L1 infers state 2 over state 3 — scalar implicature present.
Minimal access (a=1): implicature canceled — state 2 does not exceed state 3.
Partial access (a=2): implicature canceled — state 2 does not exceed state 3.
Full access: "two" → upper-bounded reading, state 2 > state 3.
Partial access (a=2): upper bound weakened — state 2 does not exceed state 3.
Full access: "one" → state 1 preferred over state 2.
Full access: "one" → state 1 preferred over state 3.
Minimal access (a=1): canceled — state 1 does not exceed state 2.
Minimal access (a=1): canceled — state 1 does not exceed state 3.
Partial access (a=2): state 1 > state 3 (partial implicature persists).
Partial access (a=2): state 1 does not exceed state 2 (still canceled).
Map each empirical finding to the RSA model prediction that accounts for it.
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The RSA model accounts for all 11 empirical findings from @cite{goodman-stuhlmuller-2013}.