Leveraging older adults’ susceptibility to distraction to improve memory for face-name associations.
Bibliographic record
Abstract
Forgetting people's names is a common memory complaint among older adults and one that is consistent with experimental evidence of age-related decline in memory for face-name associations. Despite this difficulty intentionally forming face-name associations, a recent study demonstrated that older adults hyperbind distracting names and attended faces, which produces better learning of these face-name pairs when they reappear on a memory test (Weeks, Biss, Murphy, & Hasher, 2016). The current study explored whether this effect could be leveraged as an intervention to reduce older adults' forgetting of face-name associations, using a method previously shown to improve older adults' retention of a word list (Biss, Ngo, Hasher, Campbell, & Rowe, 2013). Twenty-five younger and 32 older adults studied 24 face-name pairs and were tested via immediate and delayed memory tests. During the 30-min retention interval, 10 of the face-name pairs reoccurred as distraction in an ostensibly unrelated face-judgment task, providing an opportunity to implicitly rehearse these pairs. Older adults showed reduced forgetting of repeated face-name pairs as well as improved recollection. Younger adults showed no reliable benefit. These findings indicate that useful distraction benefits older adults' memory for face-name associations, suggesting its potential utility as a memory intervention technique. (PsycINFO Database Record
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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 | 0.002 |
| 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 | 0.000 |
| 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".