Unfamiliar face matching, within-person variability, and multiple-image arrays
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Human unfamiliar face matching is error-prone, but some research suggests matching to multiple-image arrays instead of single images may yield improvements. Here, high or low variability arrays containing one, two, and three images, and a target image from the high and low variability image sets were displayed. Arrays were presented simultaneously or sequentially, and the target image was presented simultaneously with arrays or sequentially after arrays, in three experiments. Benefits from exposure to multiple images of the same person required simultaneous viewing of images and improvements were observed in match trials only. Only sequential viewing of a multiple-image array followed by a high variability target image enhanced overall accuracy across trial types, particularly for high variability arrays. Accuracy was highest when the target image and array items were visually similar. Results show the importance of image similarity, and suggest variability is most helpful when array and target are presented sequentially.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.001 | 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 it