The role of interattribute distances in face recognition and their relation to holistic processing
Bibliographic record
Abstract
To understand the impact of interattribute distances (IADs) in face recognition, we correlated the performance of 42 participants in the Cambridge Face Memory Test (CFMT) and in the Face Composite Task to their sensitivity to IADs, as indexed by three novel tasks. In the first task, participants needed to adjust the length of an horizontal line to match the interocular distance (IOD) of a briefly presented face (1000ms). Face stimuli were shown in various sizes. White positional noise with constant energy was added independently to the xy-coordinates of the left eye, and the y-coordinates of the nose, the mouth and the left brow. The right eye and brow were kept symmetrical to the left ones. Performance was measured as the correlation between the adjusted length and the veridical IOD. In the two other tasks, two face stimuli were shown successively for 500ms, and participants had to decide if they were identical or different. The stimuli were created the same way as in the first task, except that in 50% of the trials, both faces had identical IOD (second task) or identical IADs (third task). The level of noise was adjusted to maintain an accuracy of 75% and was taken as a index of performance. The third task was significantly correlated with the CFMT (r=-.4, p< 0.01), suggesting that it reflects global visual processing capabilities in face identification. However, no significant correlation was found between our three tasks and the five indexes of the Composite Face Effect (CFE; DeGutis, et al, 2013; Konar et al., 2010; Richler, et al. 2011). This suggests that global sensitivity to IADs and the capacity to use IOD while ignoring other IADs, are not associated with holistic face processing. Specific links between the CFE and facial scaling will be tested with models on the positional noise. Meeting abstract presented at VSS 2015
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".