Changes in Regional and Temporal Patterns of Activity Associated with Aging during the Performance of a Lexical Set-Shifting Task
Bibliographic record
Abstract
Some older individuals seem to use compensatory mechanisms to maintain high-level performance when submitted to cognitive tasks. However, whether and how these mechanisms affect fronto-striatal activity has never been explored. The purpose of this study was to investigate how aging affects brain patterns during the performance of a lexical analog of the Wisconsin Card Sorting Task, which has been shown to strongly depend on fronto-striatal activity. In the present study, both younger and older individuals revealed significant fronto-striatal loop activity associated with planning and execution of set-shifts, though age-related striatal activity reduction was observed. Most importantly, while the younger group showed the involvement of a "cognitive loop" during the receiving negative feedback period (which indicates that a set-shift will be required to perform the following trial) and the involvement of a "motor loop" during the matching after negative feedback period (when the set-shift must be performed), older participants showed significant activation of both loops during the matching after negative feedback period only. These findings are in agreement with the "load-shift" model postulated by Velanova et al. (Velanova K, Lustig C, Jacoby LL, Buckner RL. 2007. Evidence for frontally mediated controlled processing differences in older adults. Cereb Cortex. 17:1033-1046.) and indicate that the model is not limited to memory retrieval but also applies to executive processes relying on fronto-striatal regions.
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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 | 0.000 |
| 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".