Impacts of normal aging on different working memory tasks: implications from an fMRI study.
Bibliographic record
Abstract
PURPOSE: To evaluate patterns of activation, convergence and divergence of three functional magnetic resonance imaging (fMRI) Working Memory (WM) tasks in two different age groups. We want to understand potential impact of task and subjects' age on WM activations as well as most important areas with regard to WM functions. MATERIALS AND METHODS: Thirty-five healthy volunteers completed visual, verbal, and novel auditory WM tasks. The subjects were selected from age extremes to depict possible impact of normal aging. General Linear Model was used to report significant activations and the effect of group. One-to-one comparison of the tasks and Combined Task Analysis was also performed. RESULTS: Most of the observed differences between the tasks were seen in areas that were responsible for feature processing. Frontal regions were mainstay activation areas, regardless of the utilized stimulus. We found an age-related reduction in activity of visual (in visually-presented tasks) and auditory (in auditory task) cortices but an age-related increase in prefrontal cortex for all tasks. CONCLUSION: Regardless of the type of the task stimuli, frontal regions are the most important activation areas in WM processing. These areas are also main targets of age-related changes with regard to activation patterns. Our results also indicate that prefrontal overactivity in working memory might be a compensatory effort to mask age-related decline in sensory processing.
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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 | 0.001 |
| 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) | 0.001 | 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".