“The Butcher on the Bus” Is Less Familiar to Older Adults Than She Is to Younger Adults
Bibliographic record
Abstract
OBJECTIVES: The "butcher on the bus" is a term describing recognizing someone as familiar but failing to recollect how we know them. Previous studies probing this phenomenon have not used paradigms that mimic real life, and age-related differences have not been adequately addressed. METHODS: In two studies, younger and older adults studied faces in scene contexts a variable number of times. In the test phases, studied faces were presented identical to the study phase, new faces were presented with new scenes, or studied faces were presented in a different context. In Study 1, this different context was the face presented with new features (pose, hairstyle, and clothing) and in a new scene. In Study 2, this different context was the same scene, but with new features. Participants made recollect, familiar, or new judgments, as well as source memory judgments for each face. RESULTS: In both studies, younger adults had better recognition discrimination and recollection. The critical finding of both studies was that when faces were presented with new features, in either the same scene or a different scene, older adults did not find these faces as familiar as did younger adults. DISCUSSION: Age-related declines in face recognition when features change may affect social interactions and everyday recognition tasks, highlighting the need for further research and supportive strategies to assist older adults in recognizing people.
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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) | 0.000 | 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".