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Record W2323316497 · doi:10.1097/wnr.0000000000000250

Electrophysiological response to omitted stimulus in sentence processing

2014· article· en· W2323316497 on OpenAlexaff
Hiroko Nakano, M. M. Rosario, Yuriko Oshima‐Takane, Lara J. Pierce, Sophie G. Tate

Bibliographic record

VenueNeuroreport · 2014
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsMcGill University
Fundersnot available
KeywordsP600SentenceStimulus (psychology)Sentence processingPsychologyCognitionPronounEvent-related potentialLinguisticsComprehensionCognitive psychologyNeuroscienceN400

Abstract

fetched live from OpenAlex

The current study provides evidence that the absence of a syntactically expected item leads to a sustained cognitive processing demand. Event-related potentials were measured at the omission of a syntactically expected object argument in a speech sequence. English monolingual adults listened to paired sentences. The first sentence in the pair established a context. The second sentence provided a response to the first sentence that was either grammatically correct by containing an overt object argument in the form of a pronoun, or was syntactically unacceptable by omitting the expected object pronoun. Event-related potentials measured at the omission of the object argument showed a prolonged positivity for 100-600 ms with a broad scalp distribution, and for 600-1000 ms with a focus in the anterior region. This observed omitted stimulus potential may contain characteristics of the P300 component, associated with the detection of the deviation of an expected stimulus, and the classical P600 related to syntactic reanalysis. Further, the late anterior P600 may indicate an increased memory demand in sentence comprehension. Thus, this linguistic omitted stimulus potential is a cognitive indicator of language processing that can be used to investigate the organization of linguistic knowledge.

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 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)0.005
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 score0.072
Threshold uncertainty score0.641

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.025
GPT teacher head0.300
Teacher spread0.274 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations7
Published2014
Admission routes1
Has abstractyes

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