Real-world familiarization: Faces become familiar through short-term naturalistic exposure
Bibliographic record
Abstract
Previous research suggests that adult recognition is impaired by the passage of time and changes in appearance, even for familiar faces. However, few studies have investigated the process of familiarization in a naturalistic setting. In the current study, we use the popular television series New Amsterdam to investigate how increased exposure can improve human recognition performance as faces transition from unfamiliar to familiar. Participants were asked to complete two old/new recognition tasks, 1 week apart. At Time 1, participants were shown ambient images of celebrities from the show New Amsterdam, one at a time, for 5 seconds each. After a distractor task, participants completed an old/new recognition task with new images of the New Amsterdam celebrities and of new celebrities. Participants were randomly assigned to one of two conditions: a) exposure, in which they were asked to watch five episodes of New Amsterdam; and b) control, in which they were asked to watch five episodes of Greys Anatomy. At Time 2, participants completed a second old/new recognition task with different images. Face recognition accuracy was measured as the hit rate (labeling an old image as old and a new image as new). Using a signal detection framework, we will calculate a sensitivity score for each participant to assess their recognition accuracy. Preliminary data from 6 adult participants reveal that overall participants were similar in accuracy when in the exposure condition (mean % = 78.1) versus the control condition (mean % = 76.6). We are currently recruiting 100 participants and expect to find greater recognition accuracy for New Amsterdam celebrity faces in the exposure condition at Time 2 compared to Time 1, but that recognition accuracy will remain the same at both times in the control condition. These results will offer insight into how faces become familiar through short-term naturalistic exposure.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".