The Effects of Divided Attention on Encoding- and Retrieval-Related Brain Activity: A PET Study of Younger and Older Adults
Bibliographic record
Abstract
Divided attention (DA) disrupts episodic encoding, but has little effect on episodic retrieval. Furthermore, normal aging is associated with episodic memory impairments, and when young adults are made to encode information under DA conditions, their memory performance is reduced and resembles that of old adults working under full attention (FA) conditions. Together, these results suggest a common neurocognitive mechanism by which aging and DA during encoding disrupt memory performance. In the current study, we used PET to investigate younger and older adults' brain activity during encoding and retrieval under FA and DA conditions. In FA conditions, the old adults showed reduced activity in prefrontal regions that younger adults activated preferentially during encoding or retrieval, as well as increased activity in prefrontal regions young adults did not activate. These results indicate that prefrontal functional specificity of episodic memory is reduced by aging. During encoding, DA reduced memory performance, and reduced brain activity in left-prefrontal and medial-temporal lobe regions for both age groups, indicating that DA during encoding interferes with encoding processes that lead to better memory performance. During retrieval, memory performance and retrieval-related brain activity were relatively immune to DA for both age groups, suggesting that DA during retrieval does not interfere with the brain systems necessary for successful retrieval. Finally, left inferior prefrontal activity was reduced similarly by aging and by DA during encoding, suggesting that the behavioral correspondence between these effects is the result of a reduced ability to engage in elaborate encoding operations.
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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".