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Age-related differences in conversational discourse abilities A comparative study

2019· article· en· W2926095828 on OpenAlexaboutno aff
Natalie Pereira, Ana Paula Bresolin Gonçalves, Mariana Goulart, Marina Tarrasconi, Renata Kochhann, Róchele Paz Fonseca

Bibliographic record

VenueDementia & Neuropsychologia · 2019
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyLinguisticsCognitive psychologyDevelopmental psychologyPhilosophy

Abstract

fetched live from OpenAlex

Conversational discourse (CD) is among the most complex tasks in everyday life and relies on multiple cognitive domains (communicative and executive abilities). Alterations in discourse comprehension and production are often present in pathological aging. However, there is still a need to identify changes in healthy aging. OBJECTIVE: This study aimed to compare young and older adults for the frequency of impaired communicative behaviors on a CD task. Performance was scored according to the Complementary Procedure of Conversational Discourse Analysis (CPCDA), developed based on the CD task from the Montreal Communication Evaluation Battery. METHODS: A total of 95 participants (54 young-adults and 41 older adults) were evaluated. The frequency of communicative behaviors was compared between groups using MANCOVA and Chi-square tests. RESULTS: Young adults showed fewer impairments in expression, pragmatics, cohesion, coherence, comprehension and emotional prosody. Older adults showed higher levels of verbal initiative and had fewer word finding difficulties. Communicative behaviors associated with planning and self-monitoring (e.g. repetition of information and syllabic false starts) appear to be common in the speech of healthy individuals in general. CONCLUSION: Studies which evaluate both discursive and cognitive skills are required to identify age-related changes. This would allow for the development of screening tools for CD assessment and preventive programs.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.264
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.059
GPT teacher head0.339
Teacher spread0.280 · 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 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

Citations20
Published2019
Admission routes1
Has abstractyes

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