Guidelines for studying developmental prosopagnosia in adults and children
Bibliographic record
Abstract
Developmental prosopagnosia (DP) is a neurodevelopmental condition characterized by severe face identity recognition problems that results from a failure to develop the mechanisms necessary for adequate face processing (Duchaine BC, Nakayama K. Developmental prosopagnosia: a window to content-specific face processing. Curr Opin Neurobiol 2006, 16:166-173.). It occurs in children and adults with normal visual acuity, and without intellectual impairments or known brain injuries. Given the importance of face recognition in daily life, and the detrimental effects of impaired face recognition, DP is an important area of study. Yet conventions for classifying individuals as DP for research purposes are poorly defined. In this focus paper, we discuss: (1) criteria for an operational definition of DP; 2) tests of face recognition and conventions for classifying individuals as DP; and 3) important considerations regarding common associations and dissociations, and cognitive heterogeneity in DP. We also highlight issues unique to studying DP in children, a relatively new endeavor that is proving to be an important complement to the work with adults. Ultimately, we hope to identify challenges researchers face when studying DP, and offer guidelines for others to consider when embarking on their own research pursuits on the topic. For further resources related to this article, please visit the WIREs website.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.014 |
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".