Long-Term Maintenance of Inhibition Training Effects in Older Adults: 1- and 3-Year Follow-Up
Bibliographic record
Abstract
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).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".