MétaCan
Menu
Back to cohort
Record W4319294474 · doi:10.1111/cogs.13251

How do Antecedent Semantics Influence Pronoun Interpretation? Evidence from Eye Movements

2023· article· en· W4319294474 on OpenAlexaff
Tiana V. Simovic, Craig G. Chambers

Bibliographic record

VenueCognitive Science · 2023
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAntecedent (behavioral psychology)PronounLinguisticsSubject pronounSemantics (computer science)Interpretation (philosophy)PsychologyObject pronounComputer scienceNatural language processingSocial psychology

Abstract

fetched live from OpenAlex

Pronoun interpretation is often described as relying on a comprehender's mental model of discourse. For example, in some psycholinguistic accounts, interpreting pronouns involves a process of retrieval, whereby a pronoun is resolved by accessing information from its linguistic antecedent. However, linguistic antecedents are neither necessary nor sufficient for interpreting a pronoun, and even when an antecedent has been introduced in earlier discourse, there is little evidence for the retrieval of linguistic form. The current study extends our understanding of pronoun interpretation by examining whether the semantics of antecedent expressions are retrieved from representations of past discourse. Participants were instructed to move displayed objects in a Visual World eye-tracking task. In some cases, the semantics of the antecedent were no longer viable after an instruction was completed (e.g., "Move the house on the left to area 12," where the result was that a different house is now the leftmost one). In this case, retrieving antecedent semantics at the point of hearing a subsequent pronoun ("Now, move it…") should entail a processing penalty. Instead, the results showed that antecedent semantics have no direct effect on interpretation, raising additional questions about the role that retrieval might play in pronoun interpretation.

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)0.001
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
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 score0.023
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.002
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.046
GPT teacher head0.348
Teacher spread0.302 · 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.

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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueCognitive ScienceSame topicNeurobiology of Language and BilingualismFrench-language works237,207