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Record W2964927220 · doi:10.1080/23273798.2019.1650945

Cognitive loads and time courses related to word order preference in Kaqchikel sentence production: an NIRS and eye-tracking study

2019· article· en· W2964927220 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 Cognition and Neuroscience · 2019
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsQueen's University
FundersJapan Society for the Promotion of Science
KeywordsWord orderSentenceWord (group theory)PreferenceProduction (economics)Order (exchange)CognitionEye trackingComputer sciencePsychologyNatural language processingLinguisticsArtificial intelligenceCognitive psychologyMathematicsStatistics

Abstract

fetched live from OpenAlex

The word order that is easiest to understand in a language generally coincides with the word order most frequently used in that language. In Kaqchikel, however, there is a discrepancy between the two: the syntactically basic VOS incurs the least cognitive load, whereas SVO is most frequently employed. This suggests that processing load is primarily determined by grammatical processes, whereas word order selection is affected by additional conceptual factors. Thus, the agent could be conceptually more salient than other elements even for Kaqchikel speakers. This hypothesis leads us to the following expectations: (1) utterance latency should be shorter for SVO sentences than for VOS sentences; (2) Kaqchikel speakers should pay more attention to agents than to other elements during sentence production; and (3) despite these, the cognitive load during sentence production should be higher for SVO than for VOS. A Kaqchikel sentence production experiment confirmed all three expectations.

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)0.001
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 score0.256
Threshold uncertainty score0.828

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.036
GPT teacher head0.329
Teacher spread0.292 · 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