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Record W4224243401 · doi:10.31234/osf.io/yqxcu

Exploring age-related changes in inter-brain synchrony during verbal communication

2022· preprint· en· W4224243401 on OpenAlexafffund
Suzanne Dikker, Emily N. Mech, Laura Gwilliams, Tessa V. West, Guillaume Dumas, Kara D. Federmeier

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsUniversité de MontréalMila - Quebec Artificial Intelligence InstituteCentre Hospitalier Universitaire Sainte-Justine
FundersInstitut de Valorisation des DonnéesNational Institute on AgingNational Science Foundation
KeywordsInterpersonal communicationComprehensionPsychologyDevelopmental psychologyCognitive psychologyHealth communicationNaturalismSocial psychologyCommunicationComputer science

Abstract

fetched live from OpenAlex

Successful communication is key to health in older age. This is true in the narrow sense of being able to gain critical information e.g., from health care providers, but also more broadly in being able to maintain social ties and pursue meaningful activities, which, in turn, are central to maintaining health and well-being. Compared to younger adults, older adults show both quantitative and qualitative changes in how information is processed and used over time to achieve comprehension. Such systematic age-related neural dissimilarities in processing dynamics and strategies raise fundamental questions about how the human brain supports cross-generational communication, especially in light of accumulating evidence linking interpersonal similarities in brain responses to communicative success. Yet despite its prevalence and tangible health-related importance, naturalistic intergenerational communication involving older adults is understudied. In this paper, we lay out why filling this research gap is critical in advancing our understanding of naturalistic communication, with implications for both science and practice.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.167
GPT teacher head0.343
Teacher spread0.176 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2022
Admission routes2
Has abstractyes

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