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Record W2025716740 · doi:10.1080/13803390490510068

Dual-Task Performance in Early Stage Dementia: Differential Effects for Automatized and Effortful Processing

2004· article· en· W2025716740 on OpenAlexaff
Margaret Crossley, Merrill Hiscock, Jeanette Beckie Foreman

Bibliographic record

VenueJournal of Clinical and Experimental Neuropsychology · 2004
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsSaskatchewan Health Research FoundationSaskatchewan Health AuthorityUniversity of Saskatchewan
Fundersnot available
KeywordsPsychologyCognitionFinger tappingTask (project management)AudiologyDevelopmental psychologyCognitive psychologyRapid automatized namingFluencyPhonological awarenessNeuroscience

Abstract

fetched live from OpenAlex

Attentional functions of individuals with early stage Alzheimer's disease (AD) and normal older adults (NC) were studied using a concurrent-task paradigm. Fourteen patients (5 men, 9 women) and 14 age- and sex-matched normal adults engaged in speeded unimanual tapping and speaking tasks during single- and dual-task trials. Speaking tasks were either relatively automatized (Speech Repetition) or relatively effortful (Speech Fluency). As single-task tapping rates were slower for the AD participants than for the NC participants, a proportional decrement score was used as an index of interference in the dual-task conditions. Interference during concurrent-task performance was greater when the cognitive task was effortful for both the NC and the AD groups. Although AD patients suffered higher levels of interference than NC participants while performing the effortful speech task, the two groups showed equivalent small changes in tapping speed while combining the automatized speaking and tapping tasks. Results suggest that a general-purpose attentional processing resource declines in the early stages of AD but dual-task performance is well-maintained when the component tasks are relatively automatized.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.418

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.031
GPT teacher head0.412
Teacher spread0.381 · 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 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

Citations22
Published2004
Admission routes1
Has abstractyes

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