@cite{sedivy-etal-1999} #
Achieving incremental semantic interpretation through contextual representation. Cognition 71(2), 109–147.
Empirical Phenomenon #
Visual-world eye-tracking with prenominal adjective instructions. Listeners hearing "the tall glass" in a display containing both a tall glass and a short glass identify the target faster — and consider an unrelated tall pitcher less — than in a display lacking the same-category contrast pair.
The paper reports three experiments. Experiment 1 (subexperiments 1A and 1B, intersective adjectives, N = 12) replicates @cite{eberhard-etal-1995}'s incremental processing finding and shows contrastive interpretation of color/material/shape adjectives. Experiments 2 (N = 24) and 3 (N = 22) probe the mechanism for vague scalar adjectives such as "tall" that lack a stable denotation, contrasting a definite-NP instruction task with an indefinite-NP verification task.
Paradigm #
Built on Paradigms.VisualWorld (@cite{huettig-rommers-meyer-2011}). The
display contains four objects (ObjectRole):
target: the intended referent (e.g. tall glass).contrastingObject: same category, opposite scale pole (short glass). Present only in the contrast condition.crossCategoryCompetitor: different category but further along the scale (pitcher, taller than the target glasses). Always present.distractor: unrelated object (key). Always present.
In the no-contrast condition the contrasting object is replaced by a second distractor. Three within-subjects manipulations are crossed:
- Contrast (
ContrastCondition): contrasting object present vs. replaced. - Typicality: target is a good vs. poor exemplar of the modified NP.
- Task (
Task): instruction with definite NP (Exp 2,directAction) vs. verification with indefinite NP (Exp 3,verification).
Architectural Role #
This file is an empirical anchor: it defines the experimental cells and
qualitative predicates that downstream theoretical models must satisfy. A
theory of incremental adjective processing predicts a numeric response for
each Cell; the predicates PredictsContrastSpeedsTarget,
PredictsContrastReducesCompetitorLooks, and
PredictsContrastAttenuatesTypicality constrain those predictions to match
the qualitative empirical patterns. F-statistics from the paper are
documented in prose at each predicate; numeric means are reproduced only
for the norming study (Table 5), which is itself an empirical baseline that
operationally defines the typicality manipulation.
Theoretical Significance #
The paper's central theoretical contrast pits two accounts of the contrast effect:
- Referential / presupposition account (@cite{altmann-steedman-1988}): the contrast effect derives from definiteness presuppositions on the modified NP.
- Lexical comparison-class account (@cite{bierwisch-1989}): scalar adjectives carry a free comparison-class variable in their lexical entry, bound by the contextually salient set.
Experiment 3's indefinite verification task removes the definiteness
presupposition while preserving the contrast effect, supporting the lexical
account. The qualitative patterns universally quantify over Task, so any
theory satisfying the patterns is committed to a task-invariant mechanism.
Typicality of the target with respect to the modifying adjective, determined empirically by the norming study (Table 5).
- goodToken : Typicality
Target is a clear exemplar of the modified NP.
- poorToken : Typicality
Target is a marginal exemplar of the modified NP.
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- SedivyEtAl1999.instDecidableEqTypicality x✝ y✝ = if h : x✝.ctorIdx = y✝.ctorIdx then isTrue ⋯ else isFalse ⋯
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- SedivyEtAl1999.instReprTypicality = { reprPrec := SedivyEtAl1999.instReprTypicality.repr }
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A condition cell in Sedivy's 2 × 2 × 2 design.
- contrast : Paradigms.VisualWorld.ContrastCondition
- typicality : Typicality
- task : Paradigms.VisualWorld.Task
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- SedivyEtAl1999.instReprCell = { reprPrec := SedivyEtAl1999.instReprCell.repr }
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- SedivyEtAl1999.instInhabitedCell.default = { contrast := default, typicality := default, task := default }
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- SedivyEtAl1999.instInhabitedCell = { default := SedivyEtAl1999.instInhabitedCell.default }
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Sedivy's displays are object arrays (four-object workspace) throughout Exps 1–3, per the paper's Methods sections. Display kind is a between-study constant, not a within-study manipulation, so this instance has no lens.
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- SedivyEtAl1999.instHasDisplayKindCell = { displayKindOf := fun (x : SedivyEtAl1999.Cell) => Paradigms.VisualWorld.DisplayKind.objectArray }
The perceptual domain targeted by Exps 2 and 3 (scalar size adjectives:
"tall", "short", "long"). Cross-study bridges use this to connect
Sedivy's findings to the comparison-class typology in
Features.PropertyDomain. Exp 1's intersective adjectives (color,
material, shape) live in different domains and are not summarised
here — see the docstring for the per-experiment breakdown.
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The size domain requires comparison-class binding, which is the
structural prerequisite for Bierwisch's lexical account of the
contrast effect (§5 of @cite{sedivy-etal-1999}). This is not a
stipulation: it follows from Features.PropertyDomain.requiresComparisonClass
by reduction.
Table 5 of @cite{sedivy-etal-1999} reports a rating study used to classify objects as good/poor tokens or contrasting objects. Subjects chose which of {target adjective, no adjective, opposite adjective, other} described each object best. Percentages reproduced here as exact rationals. These data are the operational definition of the typicality manipulation: any theory referring to "good" vs. "poor" tokens of a scalar adjective inherits this empirical baseline.
Good tokens: 92.5% of subjects chose the target-adjective description.
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- SedivyEtAl1999.normTargetAdjForGoodToken = 925 / 1000
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Good tokens: 3.3% chose the bare-noun description.
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- SedivyEtAl1999.normNoAdjForGoodToken = 33 / 1000
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Poor tokens: 19.4% chose the target-adjective description.
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- SedivyEtAl1999.normTargetAdjForPoorToken = 194 / 1000
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Poor tokens: 69.3% chose the bare-noun description.
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- SedivyEtAl1999.normNoAdjForPoorToken = 693 / 1000
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Contrasting objects: 75.2% chose the opposite-adjective description.
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- SedivyEtAl1999.normOppositeAdjForContrastObject = 752 / 1000
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Norming verification: good tokens overwhelmingly receive target-adjective descriptions; poor tokens are predominantly bare-noun. This justifies the typicality manipulation across Exps 2–3.
Norming verification: contrasting objects are reliably described by the opposite-pole adjective. This is the operational definition of "scale contrast" in the visual paradigm.
Patterns 1 and 2 are stated at the paradigm level
(Paradigms.VisualWorld.ContrastSpeedsResponse,
ContrastReducesCompetitorLooks); they consume Sedivy's
HasContrastCondition Cell instance to swap the contrast factor while
holding typicality and task fixed. Pattern 3 (typicality interaction)
is study-specific because typicality is not a paradigm primitive — it
is Sedivy's operationalisation of "exemplar goodness" via the norming
study.
Pattern 3 (Tables 6, 8, 9): contrast attenuates typicality effects.
The typicality slowdown (poor-token RTs longer than good-token RTs) is smaller in the contrast condition than in the no-contrast condition. Mechanism: when a visual contrast pair is present, the comparison class is fixed by visual context rather than by stored norms about typical exemplars; stored typicality matters less.
Empirical support:
- Exp 2 latency contrast × typicality: F₁(1,21) = 3.3, P = 0.08 (marginal). Latency typicality gap (combined): contrast 16 ms (554 − 538) vs. no-contrast 134 ms (690 − 556).
- Exp 3 yes-rate contrast × typicality: F₁(1,21) = 17.13, P < 0.001; F₂(1,19) = 19.45, P < 0.001. The interaction is most visible in yes-rate (Table 8): no-contrast-poor 35% yes vs. contrast-poor 8.3% (good token: 1.9% vs. 4.4%).
- Exp 3 yes-response latency contrast × typicality: F₁(1,21) = 8.77, P < 0.01; F₂(1,13) = 22.95, P < 0.001.
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A theory satisfies the Sedivy pattern if it predicts all three qualitative findings.
contrast_speeds_target: paradigm-levelContrastSpeedsResponse(Sedivy Tables 6, 9). Exp 2 latency main effect of contrast: F₁(1,21) = 11.62, P < 0.01. Exp 3 yes-response latency: F₁(1,21) = 20.00, P < 0.001.contrast_reduces_competitor_looks: paradigm-levelContrastReducesCompetitorLooks(Sedivy Tables 7, 11). Exp 2 F₁(1,22) = 32.66, P < 0.001. Exp 3 F₁(1,19) = 12.83, P < 0.01.contrast_attenuates_typicality: study-specific Pattern 3 above.
Task invariance is enforced by the lens-based paradigm predicates:
setContrast k c swaps the contrast condition while preserving
c.task, so the predicate quantifies uniformly over Exp 2
(directAction) and Exp 3 (verification) cells. This is the
empirical discriminator between presupposition-based and lexical
accounts of the contrast effect (the former predicts a task-by-
contrast interaction; the data show task invariance).
- contrast_speeds_target : Paradigms.VisualWorld.ContrastSpeedsResponse rt
- contrast_reduces_competitor_looks : Paradigms.VisualWorld.ContrastReducesCompetitorLooks looks
- contrast_attenuates_typicality : ContrastAttenuatesTypicality rt
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A trivial RT model: contrast cells get RT 0, no-contrast cells get RT 2,
with poor tokens adding 1 in no-contrast (so the typicality gap is
larger there). Constructed only to witness that SatisfiesSedivyPattern
is satisfiable; carries no theoretical content.
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A trivial look model: competitor looks are 1 in contrast cells and 2 in no-contrast cells; other roles get 0. Witness only.
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The Sedivy pattern is satisfiable: trivialRT and trivialLooks
jointly satisfy all three predicates. Without this witness the
structure could in principle be uninhabited.
The lexical-semantic account of the contrast effect (@cite{bierwisch-1989},
adopted in §5 of @cite{sedivy-etal-1999}) places a free comparison-class
variable in the lexical entry of every scalar adjective. The
Features.PropertyDomain infrastructure flags this with
requiresComparisonClass; the connection is made formal by
adjDomain_requires_comparison_class above.
Note: Exp 1B nonetheless found contrast effects with intersective adjectives,
attributed to referential narrowing rather than comparison-class binding.
The two mechanisms are theoretically distinct; requiresComparisonClass
captures only the comparison-class route.