Perceived age is distorted in visual memory: A phenomenon of “forward” and “backward” aging for faces
Bibliographic record
Abstract
When we meet someone, we quickly make judgments about them based on how old they look (e.g. about their physical abilities, cognitive abilities and personality traits). But how is a person’s age represented in the mind in the first place? Do we remember certain people as younger, or as older, than they actually were? One possibility is that representations of facial age exhibit ‘representational momentum’, such that observers remember a face as older than it actually was. Another possibility is that our memory for facial age is biased towards the average of the faces that we have seen previously, in which case observers might misremember faces as closer to middle age. To explore these possibilities, we ran three experiments which tested participants’ memory for the age of a briefly presented face. Participants saw a target face which was either young (30 years old) or old (60 years old). Subsequently, they saw two new faces – one 10 years younger and another 10 years older than the target. Participants selected the face that matched the target. Contrary to our initial predictions, we did not find a bias to remember faces as older, or as closer to middle age. Instead, a distinct pattern emerged — observers were biased to remember young targets as younger (i.e. ‘backward aging’), and old targets as older (i.e. ‘forward aging’). Remarkably, these biases held across sexes (male, female) and races (asian, black, white) of the target face, across artificially-aged and real faces, and regardless of the observers’ own age. Further, the results persisted even when the decoys’ identities differed from that of the target face — suggesting that this bias operates over abstract representations of age. Thus, social categories of ‘young’ and ‘old’ shape and distort our visual memories of faces.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".