Neurophysiological Measures of Task-Set Switching: Effects of Working Memory and Aging
Bibliographic record
Abstract
We investigated age-related differences in task-switching performance by using behavioral measures and event-related brain potentials. We tested younger and older adults, and we separated older adults into groups with high and low working memory (WM); that is, we separated them into old-high-WM and old-low-WM groups. On average, all participants responded more slowly in mixed-task than in single-task blocks (i.e., reaction time or RT mixing cost). Younger adults and old-high-WM participants had equivalent RT mixing costs and showed larger posterior negative slow-wave activity when preparing for mixed trials than for single-task trials, suggesting that mixed-task trials required trial-to-trial preparation. Old-high-WM participants also showed frontally distributed activity on mixed-task trials, suggesting their use of executive control to offset age-related differences in mixed-task preparation. In contrast, old-low-WM participants had large RT mixing costs and large posterior event-related brain potential negativities during single-task trials, suggesting that they prepare during single- and mixed-task blocks. High WM, therefore, may help older adults offset the age-related difficulties often observed when they are task switching.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".