The reliability, stability and consistency of individual differences across multiple face identification tasks.
Bibliographic record
Abstract
Recognizing unfamiliar faces is difficult: Two images of the same person can look different, and images of two different people can look similar. In two studies we examined individual differences on unfamiliar face identification tasks to determine whether differences in sensitivity and response bias are stable across time and tasks. We also assessed whether recognition (accurately perceiving that two images belong to the same person) and discrimination (telling two people apart) are dissociable processes. In Study 1, participants completed a battery of four unfamiliar face identification tasks that varied in task demands (sorting, same/different, line-up) and the amount of within-person variability in appearance that was present in the stimuli. Approximately one week later, participants completed a second version of the same tasks (same protocol, different stimuli). In Study 2, participants completed two versions of three unfamiliar face identification tasks in which stimuli were presented simultaneously vs. sequentially—thus, introducing memory demands. All tasks showed adequate reliability. Individual differences in sensitivity and bias were consistent across tasks and in the simultaneous vs. sequential versions. Recognition and discrimination were not dissociable when individual differences in bias were controlled for. In Study 3 (ongoing) we are investigating the extent to which performance on unfamiliar face identification tasks predicts the efficiency with which one learns a new face. Face learning efficiency is measured via the difference in recognition accuracy following each of the learning conditions (1, 3, 6, and 9 images). The results have implications for applied settings and theoretical models.
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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 | 0.006 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".