Finding the Words: How Does the Aging Brain Process Language? A Focused Review of Brain Connectivity and Compensatory Pathways
Bibliographic record
Abstract
As people age, there is a natural decline in cognitive functioning and brain structure. However, the relationship between brain function and cognition in older adults is neither straightforward nor uniform. Instead, it is complex, influenced by multiple factors, and can vary considerably from one person to another. Reserve, compensation, and maintenance mechanisms may help explain why some older adults can maintain high levels of performance while others struggle. These mechanisms are often studied concerning memory and executive functions that are particularly sensitive to the effects of aging. However, language abilities can also be affected by age, with changes in production fluency. The impact of brain changes on language abilities needs to be further investigated to understand the dynamics and patterns of aging, especially successful aging. We previously modeled several compensatory profiles of language production and lexical access/retrieval in aging within the Lexical Access and Retrieval in Aging (LARA) model. In the present paper, we propose an extended version of the LARA model, called LARA-Connectivity (LARA-C), incorporating recent evidence on brain connectivity. Finally, we discuss factors that may influence the strategies implemented with aging. The LARA-C model can serve as a framework to understand individual performance and open avenues for possible personalized interventions.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".