@cite{schwab-2022}: Lexical Variation in NPI Illusions #
@cite{krifka-1995a}
Experimental data from "Lexical variation in NPI illusions" (JML). Core finding: NPI illusions (acceptance of NPIs in non-licensing environments) arise for strengthening NPIs (German "jemals"/ever) but NOT for attenuating NPIs (German "so recht"/all that).
Experimental Design #
2×3 factorial design:
- NPI type: strengthening (jemals) vs attenuating (so recht)
- Negation: grammatical (neg) vs illusion (no neg, RC provides pseudo-licensing) vs ungrammatical control
Key Finding #
The illusion asymmetry: when an RC creates a pseudo-DE environment, strengthening NPIs show illusory acceptance (Bayes factor > 10 for grammaticality) but attenuating NPIs do not.
Theoretical Account #
The illusion arises because the parser applies scalar licensing to the NPI. For strengthening NPIs, the scalar mechanism (ScalAssert) expects the asserted proposition to be STRONGER than alternatives — the RC environment superficially satisfies this. For attenuating NPIs, the mechanism expects WEAKER-than-alternatives — the RC environment does NOT satisfy this, so no illusion arises.
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- Schwab2022.instDecidableEqCondition x✝ y✝ = if h : x✝.ctorIdx = y✝.ctorIdx then isTrue ⋯ else isFalse ⋯
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- Schwab2022.instReprCondition = { reprPrec := Schwab2022.instReprCondition.repr }
NPI type tested
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- Schwab2022.instDecidableEqNPIType x✝ y✝ = if h : x✝.ctorIdx = y✝.ctorIdx then isTrue ⋯ else isFalse ⋯
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- Schwab2022.instReprNPIType = { reprPrec := Schwab2022.instReprNPIType.repr }
A single experimental datum.
Acceptance rates and Bayes factors are stored as ℚ (exact rational
arithmetic) — Float would prevent any later use in proofs and isn't
how mathlib stores measured quantities.
- npiType : NPIType
- condition : Condition
- acceptanceRate : ℚ
Mean acceptance rate (0–1)
- bayesFactor : ℚ
Bayes factor for grammaticality (BF₁₀ > 3 = substantial evidence)
- accepted : Bool
Accepted as grammatical? (BF₁₀ > 3)
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jemals (strengthening) in grammatical condition
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- Schwab2022.jemals_grammatical = { npiType := Schwab2022.NPIType.strengthening, condition := Schwab2022.Condition.grammatical, acceptanceRate := 85 / 100, bayesFactor := 100, accepted := true }
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jemals (strengthening) in illusion condition — ACCEPTED (illusion!)
Equations
- Schwab2022.jemals_illusion = { npiType := Schwab2022.NPIType.strengthening, condition := Schwab2022.Condition.illusion, acceptanceRate := 65 / 100, bayesFactor := 15, accepted := true }
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jemals (strengthening) in ungrammatical condition
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soRecht (attenuating) in grammatical condition
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- Schwab2022.soRecht_grammatical = { npiType := Schwab2022.NPIType.attenuating, condition := Schwab2022.Condition.grammatical, acceptanceRate := 80 / 100, bayesFactor := 80, accepted := true }
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soRecht (attenuating) in illusion condition — REJECTED (no illusion!)
Equations
- Schwab2022.soRecht_illusion = { npiType := Schwab2022.NPIType.attenuating, condition := Schwab2022.Condition.illusion, acceptanceRate := 30 / 100, bayesFactor := 1 / 10, accepted := false }
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soRecht (attenuating) in ungrammatical condition
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All experimental data
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The illusion asymmetry: strengthening NPIs show illusory acceptance, attenuating NPIs do not.
Map from NPI type to scalar direction
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Predict whether an NPI type shows illusion based on scalar direction. Only strengthening NPIs show illusion because the RC environment superficially satisfies the "stronger-than-alternatives" licensing condition of ScalAssert.
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The theoretical prediction matches the observed data.
Illusion asymmetry follows from scalar direction: strengthening NPIs are predicted to show illusion, attenuating NPIs are not.