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Record W2027010908 · doi:10.1080/13825585.2015.1028326

Interacting effects of age and time of day on verbal fluency performance and intraindividual variability

2015· article· en· W2027010908 on OpenAlexafffund
Sam Iskandar, Kelly J. Murphy, Anne Baird, Robert West, Maria L. Armilio, Fergus I. M. Craik, Donald T. Stuss

Bibliographic record

VenueAging Neuropsychology and Cognition · 2015
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsCredit Valley HospitalBaycrest HospitalUniversity of TorontoOntario Brain InstituteUniversity of Windsor
FundersCanadian Institutes of Health ResearchUniversity of Toronto
KeywordsEveningMorningPsychologyVerbal fluency testFluencyDevelopmental psychologyCognitionAudiologyAge groupsNeuropsychologyDemographyMedicine

Abstract

fetched live from OpenAlex

We explored the effects of age and time of day (TOD) on verbal fluency ability with respect to performance level and intraindividual variability (IIV). Verbal fluency, which involves complex cognitive operations, was examined in 20 older (mean age = 72.8 years) and 20 younger (mean age = 24.2 years) adults with test start time alternating between morning and evening across four days. Older adults generated more words in the morning and younger adults more in the evening, corresponding with self-report peak TOD. Age by TOD interactions were also observed across fluency tasks on the number of switches among subcategory exemplars during word generation and on the IIV observed in switching behavior. Older adults exhibited greater variability in switching in the evening than in the morning, whereas younger adults showed the opposite pattern. These findings demonstrate that processes involving energization (initiating and sustaining) and attentional control may be particularly sensitive to age differences in TOD influences on cognition.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.752
Threshold uncertainty score0.369

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)0.0000.000

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 head0.063
GPT teacher head0.331
Teacher spread0.268 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations19
Published2015
Admission routes2
Has abstractyes

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