Older adult faces in the young adults' eyes: attention towards identity cues eliminates the recognition advantage for young adult faces
Bibliographic record
Abstract
Young adults typically recognize young adult faces more accurately than older adult faces (own-age bias [OAB]). However, most research on this topic has measured recognition memory for controlled images of different identities and participants typically are instructed to memorize faces during a study phase. We investigate the OAB for images incorporating within-person variability in appearance and examined whether making same/different judgements (i.e., focusing on identity cues) about face pairs during the study phase reduces the OAB. Young adults (n=24/group) completed an old/new recognition task after viewing a series of old and young faces. During the study phase, one group completed a perceptual matching task in which participants were required to decide whether two different pictures of older/young adult faces belonged to the same person or two different people. Participants in the control group viewed the same faces during the study phase but faces were presented sequentially and participants were instructed to remember them. In the identity matching task, there was no overall advantage for young faces. However, accuracy was higher for old compared to young faces on same trials (p=.031), but for young compared to older faces on different trials (p=.021). In the recognition task, participants in the control condition demonstrated the typical OAB (better memory for young relative to older faces [p=.042]); in contrast, the OAB was absent for participants who first completed the matching task (p=.491). The absence of the OAB in the identity-matching condition is attributable to identity matching improving performance for older (p=.002), but not young faces (p=.60), relative to the control condition. Collectively, these results suggest that the OAB is attributable to a failure to attend to identity cues in older faces rather than an inability to encode and store older adult faces. Meeting abstract presented at VSS 2016
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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 | 0.001 |
| 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".