Visual recognition of the "silent generation": Understanding the recognition advantage for young versus older adult faces.
Bibliographic record
Abstract
Young adults recognize own-age faces more accurately than other-age faces, but less is known about the underlying mechanisms and there is inconsistent data concerning whether older adults show an own-age or a young-adult recognition bias. In Experiment 1 we tested young adults’ (n = 20) ability to recognize upright and inverted young versus older faces in a delayed 2-alternative forced-choice (2AFC) task. We found a marginal advantage for young faces in the upright condition (p=.058) and a stronger inversion effect for young faces (p=.015), indicating greater use of face-specific processing strategies with young compared to older faces. In Experiment 2 we tested whether the young face processing advantage reflects greater sensitivity to identity cues in young faces. We blended 24 young and 24 old identities to create two average faces (one young, one old) and then morphed the average face with each original identity to create faces with different identity strengths (5 steps: 20%, 40%, 60%, 80%, 100%). In each of four blocks, young (20-30) and older (60-90) participants were trained to recognize a target face and then were asked to detect that target face or faces similar to the target (e.g., the 40% version) among other faces of the same age. Although young adults were overall more sensitive than older adults (p <.001), there was no effect of face age (p = .39), indicating that sensitivity to identity cues for both young and older adults does not differ as a function of face age in a target detection task. Accordingly, in Experiment 3 we replicated the 2AFC task at 60% identity strength and found that the processing advantage for young faces disappeared. Perhaps the own-age processing advantage in young adults reflects greater efficiency in building representations of multiple identities, this advantage is lost when identity strength is reduced. Meeting abstract presented at VSS 2013
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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 | 0.000 |
| 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".