Development of face processing
Bibliographic record
Abstract
This article reviews the development of the face-processing system from birth, during infancy and through childhood, until it becomes the sophisticated system observed in adults. We begin by discussing the following major theoretical issues concerning the development of face expertise: (1) nature versus nurture or the role of experience in face processing, (2) level of processing (i.e., global, basic, subordinate, individual) and expertise, and (3) type of processing (i.e., holistic, configural, featural). This general overview will be followed by a closer examination of individual studies that investigate the development of face processing. These studies will include a review of (1) development of differential processing of faces and objects, (2) development of differential processing of faces of different species, (3) developmental changes in processing facial identity, and (4) developmental changes in the categorization of faces. Our review of the developmental literature reveals early competence in face-processing abilities with infants presenting a preference for face stimuli and facial discrimination using featural, configural, and holistic cues. This early competence is then later refined as evidenced by age-related changes throughout childhood. Some of the refinements are likely due to the development of general cognitive abilities, whereas some others may be face-specific. WIREs Cogni Sci 2011 2 666-675 DOI: 10.1002/wcs.146 This article is categorized under: Psychology > Development and Aging.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".