Mechanisms of unfamiliar face recognition in children: when and how executive functioning matters
Bibliographic record
Abstract
Unfamiliar face recognition is a critical ability that can have significant implications, such as in legal or security contexts. Despite this, little is known about the cognitive skills that support children’s ability to accurately recognise and report unfamiliar faces and how these change with age. This research examined whether executive functioning (EF), including working memory, cognitive flexibility, response inhibition, and updating, predicts school-aged children’s performance on two face recognition tasks: an old/new recognition task (Experiment 1; N = 113) and a lineup identification task (Experiment 2; N = 121). While EF was not strongly related to recognition accuracy in either task, it was associated with children’s response bias, indicating that EF supports regulation of decision thresholds rather than memory strength. Age predicted modest improvements in discriminability, but these effects were not explained by EF, indicating that other developmental factors, such as metacognition or social understanding, may also play a role. Together, these findings suggest that EF contributes more to how children regulate and apply memory decisions than to how accurately they encode or retrieve unfamiliar 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".