Dynamic Face Perception: The Role of Expertise in Dual Processing of Features and Configuration
Bibliographic record
Abstract
Face perception is the basis of many types of social information exchange, but there is controversy over its underlying mechanisms. Researchers have theorized two processing pathways underlying facial perception: configural processing and featural processing. Featural processing focuses on the individual features of a face, whereas configural processing focuses on the spatial relations of features. To resolve the debate on the relative contribution of the two pathways in face perception, researchers have proposed a dual processing model that the two pathways contribute to two different perceptions, detecting face-like patterns and identifying individual faces. The dual processing model is based on face perception experiments that primarily use static faces. As we mostly interact with dynamic faces in real life, the generalization of the model to dynamic faces will advance our understanding of how faces are perceived in real life. This paper proposes a refined dual processing model of dynamic face perception, in which expertise in dynamic face perception supports identifying individual faces, and it is a learned behaviour that develops with age. Specifically, facial motions account for the advantages of dynamic faces, compared to static faces. This paper highlights two intrinsic characteristics of facial motions that enable the advantages of dynamic faces in face perception. Firstly, facial motion provides facial information from various viewpoints, and thus supports the generalization of face perception to the unlearned view of faces. Secondly, distinctive motion patterns serve as a cue to the identity of the face.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".