Within-person variability contributes to more durable learning of faces.
Bibliographic record
Abstract
Exposure to the natural, unsystematic within-person variability present across different encounters with a face (e.g., differences in emotion, makeup, and hairstyle) increases the likelihood the face will be recognized despite changes in appearance. In most studies, participants' memories are tested with a matching task administered shortly after exposure to a set of training images. In the real world, however, the time between when a face is first encountered and when it needs to be identified can be much longer. We hypothesized that in addition to facilitating acquisition of a representation of a face, unsystematic variability might also lead to better retention. To test this, in two experiments participants were randomly assigned to one of three training conditions: (a) no variability (still image), (b) systematic variability (changes in camera angle and pose in an otherwise constant setting), and (c) unsystematic variability (changes in hairstyle, makeup, clothing, and setting). Participants completed a sorting task 15 min and 5 days after viewing the target identity. Unsystematic variability led to better recognition than systematic variability, and this benefit was not reduced after a 5-day delay. Although participants expected their memory to be worse with a 5-day delay than with a 15-min delay, both overall accuracy and the advantage for training with unsystematic variability were virtually unaffected. The results suggest that exposure to unsystematic variability influences not only the initial acquisition of faces but also contributes to establishing a durable, flexible representation of faces in memory. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".