MétaCan
Menu
Back to cohort
Record W2498286492 · doi:10.1111/cogs.12396

Words Get in the Way: Linguistic Effects on Talker Discrimination

2016· article· en· W2498286492 on OpenAlex
Chandan Narayan, Lorinda Mak, Ellen Bialystok

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCognitive Science · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of CanadaYork University
KeywordsLinguisticsPsychologyPhilosophy

Abstract

fetched live from OpenAlex

A speech perception experiment provides evidence that the linguistic relationship between words affects the discrimination of their talkers. Listeners discriminated two talkers' voices with various linguistic relationships between their spoken words. Listeners were asked whether two words were spoken by the same person or not. Word pairs varied with respect to the linguistic relationship between the component words, forming either: phonological rhymes, lexical compounds, reversed compounds, or unrelated pairs. The degree of linguistic relationship between the words affected talker discrimination in a graded fashion, revealing biases listeners have regarding the nature of words and the talkers that speak them. These results indicate that listeners expect a talker's words to be linguistically related, and more generally, indexical processing is affected by linguistic information in a top-down fashion even when listeners are not told to attend to it.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.519
Threshold uncertainty score0.538

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0000.000

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 head0.045
GPT teacher head0.322
Teacher spread0.277 · 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