The influence of long-term memory on working memory: Age-differences in proactive facilitation and interference
Bibliographic record
Abstract
Prior learning can hinder subsequent memory, especially when there is conflict between old and new information. The ability to handle this proactive interference is an important source of differences in memory performance between younger and older participants. In younger participants, Oberauer et al. (2017) report evidence of proactive facilitation from previously learned information in a working memory task in the absence of proactive interference between long-term and working memory. In the present work we examine the generality of these findings to different stimulus materials and to older adults. Participants first learned image-word associations and then completed an image-word working memory task. Some pairs were the same as those initially learned, for which we expected facilitation relative to previously unencountered pairs. Other pairs were made up of previously learned elements in different combinations, for which we might expect interference. Younger and older participants showed similar levels of facilitation from previously learned associations relative to new pairs. In addition, older participants exhibited proactive interference from long-term to working memory, whereas younger participants exhibited facilitation, even for pairings that conflict with those learned earlier in the experiment. These findings confirm older adults' susceptibility to proactive interference and we discuss the theoretical implications of younger adults’ apparent immunity to interference.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".