Documentation

Linglib.Phenomena.Gradability.Studies.Nouwen2024

@cite{nouwen-2024} Deadjectival Intensifiers #

@cite{lassiter-goodman-2017} @cite{nouwen-2024}

"The semantics and probabilistic pragmatics of deadjectival intensifiers" Semantics and Pragmatics, Volume 17, Article 2.

Empirical Generalizations #

  1. Goldilocks effect: Negative-evaluative bases (horrible, terrible) yield high-degree intensifiers; positive-evaluative bases (pleasant, nice) yield moderate-degree intensifiers.

  2. Zwicky's generalization: Modal adjectives with negative polarity (unusual, surprising, impossible) can intensify, but their positive counterparts (usual, expected, possible) cannot.

RSA Model #

Extends @cite{lassiter-goodman-2017} threshold RSA with evaluative measures: deadjectival adverbs (horribly, pleasantly) derive their degree function from the evaluative meaning of their adjectival base.

Two-Threshold Simultaneous Model #

P_L1(h, θ, θ_e | u) ∝ P_S1(u | h, θ, θ_e) × P(h) × P(θ) × P(θ_e)

The listener jointly infers height h, adjective threshold θ, and evaluative threshold θ_e. The meaning function is an intersection:

Sequential Model (@cite{nouwen-2024}'s key innovation) #

The evaluative adverb updates the prior before the adjective threshold applies: Step 1 infers P₁(h | "horribly"), Step 2 infers P₂(h | "warm") using P₁ as prior.

RSAConfig Mapping #

Performance Note #

Uses scale n=6 (7 heights, 6 thresholds) rather than the paper's continuous distribution or @cite{lassiter-goodman-2017}'s n=10, giving 4.4× fewer L0 cells in the simultaneous model (1008 vs 4400) and 2.6× fewer in the sequential model. All qualitative Goldilocks predictions are preserved.

Intensifier degree class (Figure 2).

  • H (high): targets extreme degrees ("horribly warm" ≈ very warm)
  • M (moderate): targets moderate degrees ("pleasantly warm" ≈ nicely warm)
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      A deadjectival intensifier entry.

      Records the adverb form, its adjectival base, evaluative properties, modal status, and attestation pattern.

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                                          The Goldilocks effect: evaluative valence determines degree class.

                                          • Negative-evaluative bases yield high-degree (H) intensifiers
                                          • Positive-evaluative bases yield moderate-degree (M) intensifiers
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                                            Zwicky's generalization: modal adjectives with negative polarity can serve as intensifiers; positive-polarity modal adjectives cannot.

                                            • "unusually warm" ✓ (negative modal → attested)
                                            • "*usually warm" ✗ (positive modal → unattested)
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                                              Count of attested intensifiers

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                                                Count of unattested intensifiers

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                                                      ⟦tall⟧(θ)(x) = 1 iff height(x) > θ, specialized to scale 6.

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                                                        Height prior: discretized bell curve centered at h3 (norm for scale 6). Weights: [1, 5, 10, 20, 10, 5, 1] (sum = 52).

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                                                          Utterances about warmth with optional deadjectival intensifier.

                                                          The utterance set extends bare "warm" with intensified variants.

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                                                              Evaluative measure for "horrible" applied to the Height domain.

                                                              μ_horrible(h) = |h - norm|

                                                              Heights far from the norm (3) are evaluated as more "horrible". Agrees with Intensification.muHorrible 6 (see meaning_grounded_horribly).

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                                                                Evaluative measure for "pleasant" applied to the Height domain.

                                                                μ_pleasant(h) = norm - |h - norm|

                                                                Heights near the norm (3) are evaluated as more "pleasant". Agrees with Intensification.muPleasant 6 (see meaning_grounded_pleasantly).

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                                                                  Full meaning function.

                                                                  • bare_warm: h > θ (standard @cite{lassiter-goodman-2017})
                                                                  • horribly_warm: (h > θ) ∧ (μ_horrible(h) > θ_e)
                                                                  • pleasantly_warm: (h > θ) ∧ (μ_pleasant(h) > θ_e)
                                                                  • silent: always true
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                                                                    The local horribly_warm meaning agrees with theory-layer intensifiedMeaning for all inputs. This bridges the ℕ-valued local measures to the ℚ-valued theory-layer Intensification.muHorrible.

                                                                    The local pleasantly_warm meaning agrees with theory-layer intensifiedMeaning for all inputs. This bridges the ℕ-valued local measures to the ℚ-valued theory-layer Intensification.muPleasant.

                                                                    Constant evaluative measure (no evaluative content).

                                                                    Models adverbs like "*usually" — a constant measure provides no discriminating information about degree, which is why "*usually warm" is vacuous (Zwicky's generalization).

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                                                                      A constant evaluative measure provides no information: for any two heights, the measure value is identical.

                                                                      Intensified utterances are costlier than bare utterances.

                                                                      assumes that "horribly warm" has higher production cost than "warm" because it contains more morphological material. This cost differential drives the pragmatic reasoning.

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                                                                        S1 scoring rule: exp(α · (log L0(h|u,θ,θ_e) − C(u))), gated at L0=0. Identical to @cite{lassiter-goodman-2017}'s beliefAction but with Latent = Threshold × Threshold for the dual-threshold model.

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                                                                          theorem RSA.Nouwen2024.intensifierS1Score_nonneg (l0 : UtteranceHeight) (α : ) (l : Threshold × Threshold) (w : Height) (u : Utterance) :
                                                                          (∀ (u' : Utterance) (w' : Height), 0 l0 u' w')0 < α0 intensifierS1Score l0 α l w u
                                                                          @[reducible]

                                                                          RSAConfig for the simultaneous dual-threshold model.

                                                                          Extends @cite{lassiter-goodman-2017} threshold RSA with a second threshold for the evaluative adverb. L1 jointly infers height, adjective threshold, and evaluative threshold:

                                                                          P_L1(h, θ, θ_e | u) ∝ P_S1(u | h, θ, θ_e) · P(h) · P(θ) · P(θ_e)

                                                                          The meaning function uses local ℕ-valued evaluative measures, proved equivalent to Intensification.intensifiedMeaning by meaning_grounded_horribly and meaning_grounded_pleasantly.

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                                                                            H-adverb: "horribly warm" shifts height toward extremes #

                                                                            The Goldilocks effect for negative-evaluative bases: μ_horrible(h) = |h − norm| peaks at extremes, so L1 hearing "horribly warm" concentrates probability on extreme heights. Heights near the norm (h=3) have μ_horrible = 0 and cannot satisfy the evaluative positive form at any θ_e.

                                                                            M-adverb: "pleasantly warm" concentrates at moderate heights #

                                                                            The Goldilocks effect for positive-evaluative bases: μ_pleasant(h) = norm − |h − norm| peaks at the norm (h=3), so L1 hearing "pleasantly warm" concentrates probability on moderate heights. Extreme heights (h=5,6) have low μ_pleasant and cannot satisfy the evaluative positive form.

                                                                            Cross-utterance Goldilocks predictions #

                                                                            The core Goldilocks effect is a cross-utterance phenomenon: intensifiers redistribute probability mass relative to the bare adjective. "Horribly warm" assigns MORE probability to extreme heights than "warm" does; "pleasantly warm" assigns MORE to moderate heights than "warm" does.

                                                                            At extreme heights, "horribly warm" assigns more probability than "warm".

                                                                            At moderate heights, "pleasantly warm" assigns more probability than "warm".

                                                                            Sequential Dual-Threshold Model #

                                                                            key theoretical contribution: the evaluative adverb and base adjective apply sequentially rather than simultaneously. The listener first updates beliefs via the evaluative measure, then applies the adjective threshold to the resulting posterior:

                                                                            Step 1: P₁(h | "horribly") ∝ P_S1(eval_pos | h, θ_e) · P(h) · P(θ_e) Step 2: P₂(h | "warm") ∝ P_S1(warm | h, θ) · P₁(h) · P(θ)

                                                                            This sequential model makes the same Goldilocks predictions as the simultaneous model for simple cases, but differs for complex constructions (e.g., "horribly pleasantly warm").

                                                                            Implementation #

                                                                            Two RSAConfigs composed: the evaluative step's L1 posterior feeds as the adjective step's worldPrior. This uses RSAConfig composition rather than the Ctx/transition machinery, which is designed for sequential production (word-by-word S1 choices), not sequential comprehension (listener updating beliefs step by step).

                                                                            Utterances for the evaluative step: either the evaluative positive form ("the degree is horribly/pleasantly X") or silence.

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                                                                                def RSA.Nouwen2024.evalMeaning (evalMu : Height) (u : EvalUtterance) (h : Height) (θ_e : Threshold) :

                                                                                Evaluative meaning for step 1. The evaluative positive form checks only μ_eval(h) > θ_e, without the base adjective. This is the decomposed evaluative component.

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                                                                                  Evaluative step cost: the evaluative adverb costs 1, silence costs 0.

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                                                                                    noncomputable def RSA.Nouwen2024.evalS1Score :
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                                                                                      theorem RSA.Nouwen2024.evalS1Score_nonneg (l0 : EvalUtteranceHeight) (α : ) (l : Threshold) (w : Height) (u : EvalUtterance) :
                                                                                      (∀ (u' : EvalUtterance) (w' : Height), 0 l0 u' w')0 < α0 evalS1Score l0 α l w u
                                                                                      @[reducible]

                                                                                      RSAConfig for the evaluative step with a given measure function.

                                                                                      L1 hears the evaluative form and infers a posterior over heights, marginalizing over the evaluative threshold θ_e.

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                                                                                        Utterances for the adjective step.

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                                                                                            noncomputable def RSA.Nouwen2024.adjCostR (u : AdjUtterance) :
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                                                                                              noncomputable def RSA.Nouwen2024.adjS1Score :
                                                                                              (AdjUtteranceHeight)ThresholdHeightAdjUtterance
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                                                                                                theorem RSA.Nouwen2024.adjS1Score_nonneg (l0 : AdjUtteranceHeight) (α : ) (l : Threshold) (w : Height) (u : AdjUtterance) :
                                                                                                (∀ (u' : AdjUtterance) (w' : Height), 0 l0 u' w')0 < α0 adjS1Score l0 α l w u
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                                                                                                RSAConfig for the adjective step with an evaluative posterior as worldPrior.

                                                                                                The worldPrior is the L1 posterior from the evaluative step: the listener has already processed the evaluative adverb and now hears "warm". This implements the sequential composition:

                                                                                                P₂(h | "warm") ∝ P_S1("warm" | h, θ) · L1_eval(h | eval_pos) · P(θ)

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                                                                                                  noncomputable def RSA.Nouwen2024.seqL1_horribly (h : Height) :

                                                                                                  Sequential L1 posterior for "horribly warm": evaluative step uses μ_horrible, then adjective step applies the base "warm" meaning.

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                                                                                                    noncomputable def RSA.Nouwen2024.seqL1_pleasantly (h : Height) :

                                                                                                    Sequential L1 posterior for "pleasantly warm": evaluative step uses μ_pleasant, then adjective step applies the base "warm" meaning.

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                                                                                                      Sequential Goldilocks: same qualitative predictions as simultaneous #

                                                                                                      The sequential model produces the same Goldilocks pattern: "horribly warm" shifts probability toward extremes, "pleasantly warm" concentrates at moderate heights. The sequential decomposition affects the quantitative distribution but preserves the qualitative ordering.

                                                                                                      Sequential "pleasantly warm" prefers moderate heights (Goldilocks).

                                                                                                      Zwicky Vacuity: Derived from RSA #

                                                                                                      §5: Positive modal adverbs (*usually, *expectedly) cannot serve as intensifiers because their evaluative measure is constant across heights, providing no discriminating information about degree. In the sequential model, the evaluative step with a constant measure preserves the prior's bell-curve shape — "usually warm" offers no intensifying information beyond bare "warm".

                                                                                                      In contrast, negative modal measures (unusual ≈ horrible) peak at extremes, shifting the evaluative posterior away from the norm and producing genuine intensification. This is why negative modals pattern with negative evaluatives in the Goldilocks effect.

                                                                                                      The theorems below derive Zwicky's generalization from the sequential RSA model, connecting the data-layer check (zwickyHolds) to L1 posterior predictions.

                                                                                                      ℕ-valued constant measure for the sequential model. Models "usual": μ_usual(h) = 3 for all h (no height discrimination).

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                                                                                                        μ_unusual has the same shape as μ_horrible: peaks at extremes. Negative modals pattern with negative evaluatives because both assign high values to heights far from the norm.

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                                                                                                          Negative modal and negative evaluative measures are structurally identical. This is the semantic foundation of why both types make good intensifiers (§5: "the corresponding measure function has a shape similar to that of negative evaluatives").

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                                                                                                          Bare adjective RSAConfig: "warm" vs silence with the original height prior. This is the baseline — what "warm" means without any evaluative step.

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                                                                                                            Evaluative Step: Constant vs Extreme Measures #

                                                                                                            Constant-measure evaluative step preserves the prior's peak at the norm.

                                                                                                            Extreme measure (unusual/horrible) boosts extreme heights in L1 beyond what the constant measure assigns.

                                                                                                            Sequential Model: Zwicky Predictions #

                                                                                                            "Usually warm" preserves moderate-height preference (like bare "warm").

                                                                                                            "Unusually warm" shifts toward extremes (like "horribly warm"). Note: muUnusualN = muHorrible by muUnusualN_eq_muHorrible, so this is structurally the same prediction as seq_horribly_shifts_upward.

                                                                                                            Zwicky's generalization, derived: at extreme heights, "unusually warm" assigns more probability than "usually warm". Negative modal intensifiers are more informative than positive modal ones because μ_unusual discriminates heights while μ_usual does not.

                                                                                                            Converse: at moderate heights, "usually warm" dominates "unusually warm". The constant measure concentrates mass near the prior peak, while the extreme measure depletes mass at moderate heights.

                                                                                                            Bare "warm" baseline: prefers moderate heights (deg 4 > deg 6). Demonstrates that the bare model and "usually warm" agree qualitatively.