Associative interference in older and younger adults.
Bibliographic record
Abstract
Healthy older adults are more challenged by associative interference than younger adults, but prior results could have been due to differences in list discrimination ability. We used a procedure that assessed interference without requiring knowledge of list membership to test the hypothesis that older adults (60-74 years old) would show more pronounced effects of associative interference in AB/AC learning. Despite our use of a self-paced, rather than timed, study procedure, older adults performed at lower levels of accuracy than younger adults, replicating the well established associative deficit in aging (Naveh-Benjamin & Mayr, 2018). Older participants also displayed more proactive interference on average. Older participants' memory for AB and AC showed statistical independence, resembling earlier data from younger participants with a timed study procedure (Burton, Lek, & Caplan, 2017). However, younger participants, with the current self-paced procedure, produced a facilitating relationship between memory for AB and AC. Thus, younger participants not only resolved, but reversed associative interference. List discrimination could not explain these age differences. Taken together, these results extend the associative deficit in aging, finding increased susceptibility to associative proactive interference and less resolution of associative interference in older than younger participants, even when given the opportunity to compensate during self-paced study. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 0.001 |
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".