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Record W4307794460 · doi:10.32920/21440817.v1

Long-Term Maintenance of Inhibition Training Effects in Older Adults: 1- and 3-Year Follow-Up

2022· preprint· en· W4307794460 on OpenAlexafffund
Andrea Wilkinson, Lixia Yang

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldNeuroscience
TopicMind wandering and attention
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStroop effectSession (web analytics)Task (project management)PsychologyExecutive functionsCognitionMedicineGerontologyPhysical therapyPsychiatry

Abstract

fetched live from OpenAlex

<p>Objectives: The aim of this study is to examine the long-term maintenance of training benefits in inhibition, as measured with the Stroop task, in older adults over 1- and 3-year periods. Methods: Participants from an original 6-session Stroop training study (Wilkinson & Yang, 2012 [Wilkinson, A. J., & Yang, L. (2012). Plasticity of inhibition in older adults: Retest practice and transfer effects. Psychology and Aging, 27, 606–615. doi:10.1037/a0025926]) were invited to come back to the lab to complete a single session of the Stroop task at 2 different time points. Thirty-three older adults returned for the 1-year follow-up session, and 26 of them returned for the 3-year follow-up session. Results: The results revealed maintenance of the training-induced inhibition gains at both follow-up sessions. Furthermore, performance at the 2 follow-up sessions was better (i.e., reduced Stroop ratio interference score) than baseline level. Discussion: The findings demonstrate the durability of inhibition training gains in older adults for up to a 3-year period. These results further extend the literature on long-term maintenance of cognitive training benefits in older adults by examining the durability of training effects in inhibition, an important executive function, and by covering a substantial maintenance period (i.e., up to 3 years). </p>

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.482
Threshold uncertainty score0.681

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 head0.025
GPT teacher head0.263
Teacher spread0.238 · 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

Citations4
Published2022
Admission routes2
Has abstractyes

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