Proactive and retroactive interference in young adults, healthy older adults, and older adults with amnestic mild cognitive impairment
Bibliographic record
Abstract
This study investigated memory interference in amnestic mild cognitive impairment (aMCI) and normal aging. Participants were 27 young adults, 44 healthy older adults, and 15 older adults with aMCI. Memory interference was examined on the California Verbal Learning Test (CVLT) and on a modified AB-AC paradigm. Despite significant differences in memory performance on the CVLT, interference measures on this test did not distinguish individuals with aMCI and healthy older adults. The AB-AC task involved first learning a list (AB list) of 12 semantically related word pairs (e.g., knee-bone). Twenty minutes later, a second list (AC list) was learned in which the same stimulus words were paired with new response words (e.g., knee-bend). Both lists were repeated until 100% accurate recall was achieved. Finally, participants recalled the first (AB) list. Proactive interference (PI) was greater among older than younger adults, and greater still among individuals with aMCI, but the older and aMCI groups showed similar levels of retroactive interference. This study suggests that PI contributes to the memory deficits seen in aMCI and that tests sensitive to PI may assist in the early identification of aMCI. Memory interventions aimed at alleviating PI may improve memory functioning in individuals with aMCI. (JINS, 2009, 15, 83-93.).
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".