Differences in Face Recognition Ability Predicts Patterns of Holistic Face Processing in Children
Bibliographic record
Abstract
DeGutis and colleagues (2012) have demonstrated that adults with developmental prosopagnosia show a typical holistic processing effect for the mouth, but not for the eye region. These findings are consistent with previous speculations that holistic processing of the eye region may be particularly important for successful face recognition. The present study examined 30 children, recruited based on their exceptionally high or low scores on the Cambridge Face Memory Task–Children (CFMT-C) from a database of more than 500 children. These children were separated into two groups roughly matched for age: those that performed very well at face recognition (high performers; N=15) and those that performed very poorly at face recognition (low performers; N=15). Average scores on the CFMT-C for each group were 91.31% (sd=5%) and 69.27% (sd=8%) respectively. For each group, we examined holistic face processing using the Part-Whole Task (Tanaka et al., 2010). This task examines the ability to recognize face parts (e.g. the eyes) both in isolation and in the context of the whole face, both of which differed by only one feature. As expected, the high performing group showed an overall holistic advantage, with greater accuracy on whole trials than part trials [t(14)=3.82, p<0.01]. This finding remained marginally significant when examining eye and mouth trials separately [Eye Trials: t(14)=1.97, p=0.068; Mouth Trials: t(14)=2.01, p=0.064]. Similar to the findings of DeGutis and colleagues (2012), the low performing group also showed an overall holistic advantage [t(14)=3.74, p<0.01]. However, this holistic advantage was carried by a holistic effect for mouth trials [t(14)=3.1, p<0.01] but not eye trials [t(14)= -0.29, n.s.]. These results replicate the finding that holistic processing of the eye region is particularly important for successful face recognition and may be impaired in cases of prosopagnosia. Furthermore, these data demonstrate the similarities in holistic processing between children and adults. Meeting abstract presented at VSS 2014
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.
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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".