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Record W4318816636 · doi:10.3389/fpsyg.2023.1026638

Older adults’ refusal speech act in cognitive assessment: A multimodal pragmatic perspective

2023· article· en· W4318816636 on OpenAlexaboutno aff
Lihe Huang, Huiyu Qu, Deyu Zhou

Bibliographic record

VenueFrontiers in Psychology · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsnot available
FundersNational Social Science Fund of China
KeywordsPsychologyPerspective (graphical)Speech actCognitionCognitive psychologyIndirect speechLinguisticsArtificial intelligenceNeuroscienceComputer science

Abstract

fetched live from OpenAlex

This paper explores how older adults with different cognitive abilities perform the refusal speech act in the cognitive assessment in the setting of memory clinics. The refusal speech act and its corresponding illocutionary force produced by nine Chinese older adults in the Montreal Cognitive Assessment-Basic was annotated and analyzed from a multimodal perspective. Overall, regardless of the older adults' cognitive ability, the most common discursive device to refuse is the demonstration of their inability to carry out or continue the cognitive task. Individuals with lower cognitive ability were found to perform the refusal illocutionary force (hereafter RIF) with higher frequency and degree. Additionally, under the pragmatic compensation mechanism, which is influenced by cognitive ability, multiple expression devices (including prosodic features and non-verbal acts) interact dynamically and synergistically to help older adults carry out the refusal behavior and to unfold older adults' intentional state and emotion as well. The findings indicate that both the degree and the frequency of performing the refusal speech act in the cognitive assessment are related to the cognitive ability of older adults.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.536
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.379
Teacher spread0.348 · 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 designQualitative
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

Citations4
Published2023
Admission routes1
Has abstractyes

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