Effect of aging and neurodegeneration on contextual processing
Bibliographic record
Abstract
Background Contextual processing (or context processing; CP) is an integral component of cognition. Contextual processing allows people to manage their thoughts and actions by adjusting to surroundings. The process involves the formation of internal representations of context in relation to the environment, maintenance of information over a period of time, and the updating of mental representations to reflect environmental changes. Each of these functions can be affected by aging and associated brain conditions. Here, we update the current research investigating the impact of aging and neurodegeneration on CP. Method Through searching the PubMed, PsycINFO, and Google Scholar databases, 18 studies that focused on aging, mild cognitive impairment (MCI), Alzheimer’s disease (AD), and Parkinson's disease (PD) impacts on CP were retrieved and reviewed in detail. Result Older adults of normal aging had a delayed onset and reduced amplitude of electrophysiological response to information detection, comparison, and execution. MCI patients demonstrated clear signs of impaired CP compared to normal aging. The only study reporting CP in AD suggested a decreased proactive control in maintaining contextual information, but seemingly intact reactive control. On the other hand, PD without dementia showed limited ability to use contextual information in cognitive and motor processes, exhibiting impaired reactive control. Conclusion Data suggest that accelerated aging and neurodegeneration can further impact the changes in CP with age, providing insights for improving intervention strategies. This study highlights the need for increased attention to research this important but understudied field.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".