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Record W3126909198 · doi:10.3389/fpsyg.2021.611466

Pronouns Are as Sensitive to Structural Constraints as Reflexives in Early Processing: Evidence From Visual World Paradigm Eye-Tracking

2021· article· en· W3126909198 on OpenAlexafffund
Chung–hye Han, Keir Moulton, Trevor Block, Holly Gendron, Sander Nederveen

Bibliographic record

VenueFrontiers in Psychology · 2021
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsUniversity of British ColumbiaUniversity of TorontoSimon Fraser University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPronounEye trackingAntecedent (behavioral psychology)PsychologyReflexive pronounVerbLinguisticsArgument (complex analysis)ReflexivityNounNoun phraseObject (grammar)Cognitive psychologyComputer scienceArtificial intelligenceSocial psychologyPhilosophy

Abstract

fetched live from OpenAlex

A number of studies in the extant literature report findings that suggest asymmetry in the way reflexive and pronoun anaphors are interpreted in the early stages of processing: that pronouns are less sensitive to structural constraints, as formulated by Binding Theory, than reflexives, in the initial antecedent retrieval process. However, in previous visual world paradigm eye-tracking studies, these conclusions were based on sentences that placed the critical anaphors within picture noun phrases or prepositional phrases, which have independently been shown not to neatly conform to the Binding Theory principles. We present results from a visual world paradigm eye-tracking experiment that show that when critical anaphors are placed in the indirect object position immediately following a verb as a recipient argument, pronoun and reflexive processing are equally sensitive to structural constraints.

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
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.001
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 head0.045
GPT teacher head0.405
Teacher spread0.360 · 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

Citations5
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueFrontiers in Psychology→Same topicNeurobiology of Language and Bilingualism→French-language works237,207→