The effect of training on the recognition of faces across changes in viewpoint
Bibliographic record
Abstract
Recognizing a face from a novel viewpoint requires processing the structural properties of the face that are reliable cues to identity and view-invariant. One such property may be second-order relations (e.g., spacing between eyes and mouth). In Experiment 1, we investigated whether 10-year-old children's and adults' recognition of faces across changes in viewpoint could be improved through training, and whether training results were correlated with sensitivity to second-order relations. Over two one-hour sessions 10-year-olds and adults (n = 10) were trained to make same/different judgments about facial identity between faces seen from different viewpoints. Consistent with previous studies (e.g. Mondloch et al., 2003), 10-year-olds were worse overall than adults. However, both groups improved at a similar rate during training, with 10-year-olds' final accuracy being comparable to adults' accuracy prior to training. There was no correlation between performance on the viewpoint training task and sensitivity to second-order relations either before or after training in either age group, perhaps because observers may have learned to match specific views of the training faces and not a general skill. In Experiment 2, we investigated whether training adults (n = 12) would be more effective if novel faces were introduced as training progressed over the two-day period. Improvement in matching faces across changes in viewpoint transferred from the first 7 facial identities to the next 7 identities, a result suggesting that training improved a general skill in view-invariant recognition. However, improvement failed to transfer to the third set of 7 identities and was not correlated with sensitivity to second-order relations, results suggesting that the learning also involved the linking of view-specific exemplars. Collectively, the results indicate that improvements in recognizing faces across changes in viewpoint involve both view-specific and view-independent processes, and are not directly related to sensitivity to second-order relations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".