Aging, Culture, and Memory for Categorically Processed Information
Bibliographic record
Abstract
Objectives. Literature on cross-cultural differences in cognition suggests that categorization, as an information processing and organization strategy, was more often used by Westerners than by East Asians, particularly for older adults. This study examines East-West cultural differences in memory for categorically processed items and sources in young and older Canadians and native Chinese with a conceptual source memory task (Experiment 1) and a reality monitoring task (Experiment 2). Method. In Experiment 1, participants encoded photographic faces of their own ethnicity that were artificially categorized into GOOD or EVIL characters and then completed a source memory task in which they identified faces as old-GOOD, old-EVIL, or new. In Experiment 2, participants viewed a series of words, each followed either by a corresponding image (i.e., SEEN) or by a blank square within which they imagined an image for the word (i.e., IMAGINED). At test, they decided whether the test words were old-SEEN, old-IMAGINED, or new. Results. In general, Canadians outperformed Chinese in memory for categorically processed information, an effect more pronounced for older than for young adults. Discussion. Extensive exercise of culturally preferred categorization strategy differentially benefits Canadians and reduces their age group differences in memory for categorically processed information.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".