Context-updating processes facilitate response reprogramming in younger but not older adults.
Bibliographic record
Abstract
The current study used concurrent acquisition of motion capture and event-related potential (ERP) data to test the prediction that response reprogramming relies on context-updating processes, and that age differences in conflicting-response performance are related to context-updating deficits in the elderly. Participants performed a motor sequencing task that included prepotent pairs of key presses, and conflicting pairs that started with the same first key press of the prepotent pair, but ended in an unexpected alternate response. ERP analyses were used to measure the P3b component as an electrophysiological correlate of context updating. The results revealed an age-related reduction in the ability to reprogram a response as younger, but not older, adults exhibited a negative correlation between planning and execution time for conflicting responses, such that shortened execution time led to better performance by the younger group. Both age groups demonstrated a large P3b component following conflicting, but not prepotent stimuli. The peak of this P3b was delayed, and its amplitude reduced in the older, compared with younger, adults. Noteworthy was that conflicting responses with faster execution time were associated with a larger P3b component than responses with slower execution time in younger, but not older, adults, suggesting that better context updating led to more efficient response reprogramming. These findings are novel in showing that context updating is associated with adjustments in response execution, and that older adults were less able to use these context-updating processes to support successful movement reprogramming.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".