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Record W2760027377 · doi:10.1111/lang.12257

Beat Gestures and Syntactic Parsing: An ERP Study

2017· article· en· W2760027377 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueLanguage Learning · 2017
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsUniversité de MontréalCentre for Research on Brain Language and Music
FundersEuropean Research CouncilH2020 Marie Skłodowska-Curie ActionsMinisterio de Economía y Competitividad
KeywordsP600ProsodyParsingSentenceGestureSpeech recognitionN100Sentence processingPsychologyElectroencephalographyComputer scienceEvent-related potentialNatural language processingArtificial intelligenceN400Neuroscience

Abstract

fetched live from OpenAlex

Abstract We tested the prosodic hypothesis that the temporal alignment of a speaker's beat gestures in a sentence influences syntactic parsing by driving the listener's attention. Participants chose between two possible interpretations of relative‐clause (RC) ambiguous sentences, while their electroencephalogram (EEG) was recorded. We manipulated the alignment of the beat within sentences where auditory prosody was removed. Behavioral performance showed no effect of beat placement on the sentences’ interpretation, while event‐related potentials (ERPs) revealed a positive shift of the signal in the windows corresponding to N100 and P200 components. Additionally, post hoc analyses of the ERPs time locked to the RC revealed a modulation of the P600 component as a function of gesture. These results suggest that beats modulate early processing of affiliate words in continuous speech and potentially have a global impact at the level of sentence‐parsing components. We speculate that beats must be synergistic with auditory prosody to be fully consequential in behavior.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.304
Threshold uncertainty score0.839

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.040
GPT teacher head0.353
Teacher spread0.313 · 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