How Processing of Background Context Can Improve Memory for Target Words in Younger and Older Adults
Bibliographic record
Abstract
We examined how explicit instructions to encode visual context information accompanying visually-presented unrelated target words affected later recognition of the targets presented alone, in younger and older adults. In Experiments 1 and 3, neutral context scenes, and in Experiments 2 and 4, emotionally salient context scenes, were paired with target words during encoding. Experiments 1 and 2 data were collected using within subject design; in Experiments 3 and 4 we used a between subjects design. Across all four experiments, instructions to explicitly make a link (associate) between simultaneously presented context and target words always led to significantly better recognition memory in both younger and older adults compared to deep or shallow levels of processing (LoP) instructions for the context information. In all experiments the age-related deficit in overall memory remained. There was no consistent difference in the effect of a shallow versus deep processing of context in the first three experiments in young adults, although a standard LoP effect, with better memory performance following deep than shallow processing, was demonstrated with both age groups in Experiment 4. Results suggest that an instruction to explicitly link target words to context information will significantly and consistently improve memory recognition for targets. This was demonstrated in all four experiments, in both younger and older adults. Importantly, results suggest that memory in older adults can be improved with specific instructional manipulations during encoding.
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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 | 0.001 |
| 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".