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The Wind Chilled the Spectators, but the Wine Just Chilled: Sense, Structure, and Sentence Comprehension

2009· article· en· W1971566536 on OpenAlexaff
Mary Hare, Jeffrey L. Elman, Tracy Tabaczynski, Ken McRae

Bibliographic record

VenueCognitive Science · 2009
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsWestern University
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsTransitive relationLinguisticsVerbSentencePsychologyComprehensionContext (archaeology)Cognitive psychologyMathematics

Abstract

fetched live from OpenAlex

Anticipation plays a role in language comprehension. In this article, we explore the extent to which verb sense influences expectations about upcoming structure. We focus on change of state verbs like shatter, which have different senses that are expressed in either transitive or intransitive structures, depending on the sense that is used. In two experiments we influence the interpretation of verb sense by manipulating the thematic fit of the grammatical subject as cause or affected entity for the verb, and test whether readers' expectations for a transitive or intransitive structure change as a result. This sense-biasing context influenced reading times in the post-verbal regions. Reading times for transitive sentences were faster following good-cause than good theme subjects, but the opposite pattern was found for intransitive sentences. We conclude that readers use sense-contingent subcategorization preferences during on-line comprehension.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.023
GPT teacher head0.288
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations43
Published2009
Admission routes1
Has abstractyes

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