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Record W2523301982 · doi:10.3389/fpsyg.2016.01375

Punctuation and Implicit Prosody in Silent Reading: An ERP Study Investigating English Garden-Path Sentences

2016· article· en· W2523301982 on OpenAlexafffund
John E. Drury, Shari R. Baum, Hope Valeriote, Karsten Steinhauer

Bibliographic record

VenueFrontiers in Psychology · 2016
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsGlenrose Rehabilitation HospitalAlberta Health ServicesCentre for Research on Brain Language and MusicMcGill University
FundersCanadian Institutes of Health ResearchSocial Sciences and Humanities Research Council of CanadaCanada Research ChairsCanada Foundation for Innovation
KeywordsCovertPunctuationPsychologyProsodyReading (process)ParsingAmbiguitySet (abstract data type)LinguisticsCognitive psychologyComputer scienceNatural language processing

Abstract

fetched live from OpenAlex

This study presents the first two ERP reading studies of comma-induced effects of covert (implicit) prosody on syntactic parsing decisions in English. The first experiment used a balanced 2 x 2 design in which the presence/absence of commas determined plausibility (e.g., John, said Mary, was the nicest boy at the party versus John said Mary was the nicest boy at the party). The second reading experiment replicated a previous auditory study investigating the role of overt prosodic boundaries in closure ambiguities (Pauker et al., 2011). In both experiments, commas reliably elicited CPS components and generally played a dominant role in determining parsing decisions in the face of input ambiguity. The combined set of findings provides further evidence supporting the claim that mechanisms subserving speech processing play an active role during silent reading.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.033
GPT teacher head0.329
Teacher spread0.296 · 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

Citations23
Published2016
Admission routes2
Has abstractyes

Explore more

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