Older adults display diminished error processing and response in a continuous tracking task
Bibliographic record
Abstract
Advancing age is often accompanied by a decline in motor control that results in a decreased ability to successfully perform motor tasks. While there are multiple factors that contribute to age-related deficits in motor control, one unexplored possibility is that age-related deficits in our ability to evaluate motor output result in an increase in motor errors. In line with this, previous work from our laboratory demonstrated that motor errors evoked an error-related negativity (ERN)-a component of the human ERP associated with error evaluation originating within the human medial-frontal cortex. In the present study, we examined whether or not deficits in the medial-frontal error evaluation system contribute to age-related deficits in motor control. Two groups of participants (young, old) performed a computer-based tracking task that paralleled driving while EEG data were recorded. Our results show that older adults committed more behavioral errors than young adults during performance of the tracking task. An analysis of our ERP data revealed that the amplitude of the ERN was reduced in older adults relative to young adults following motor errors. Our results make an important extension from previous work demonstrating age-related reductions in the ERN during performance of cognitive tasks. Importantly, our results imply the possibility of understanding motor deficits in older age.
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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".