Age differences in memory for names and occupations associated with faces: the effects of assigned and self-perceived social importance
Bibliographic record
Abstract
It has been documented that older adults’ memory deficits can be reduced for information depicted as personally and socially important (e.g., Hargis & Castel, 2017 [Younger and older adults’ associative memory for social information: The role of information importance. Psychology and Aging, 32(4), 325–330]). The current study aimed to further assess the effects of both arbitrarily assigned and self-perceived importance in younger and older adults’ memory for names (low in schematic support) and occupations (high in schematic support) associated with faces. Participants studied the same 16 face-name-occupation triplets (with neutral facial expressions) across four blocks, each including a free recall of names and occupations. At the end, they completed a cued recall of names and occupations. The faces were arbitrarily cued as socially important (i.e., with an orange frame) or unimportant (e.g., without a frame). The perceived social importance was assessed by rating all the triplets based on a 10-point Likert Scale (1 = least and 10 = most important) at the end. The results showed that age-related memory deficits were reduced or even eliminated for occupations (high in schematic support) relative to names (low in schematic support), especially in the free recall of faces self-perceived as important. In other words, the combination of schematic support and self-perceived importance can effectively mitigate older adults’ memory deficit.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".