Elastic facial movement influences part-based but not holistic processing.
Bibliographic record
Abstract
Face processing has been studied for decades. However, most of the empirical investigations have been conducted using static face images as stimuli. Little is known about whether static face processing findings can be generalized to real-world contexts in which faces are constantly moving. The present study investigated the nature of face processing (holistic vs. part-based) in elastic moving faces. Specifically, we focused on whether elastic moving faces, as compared with static ones, can facilitate holistic or part-based face processing. Using the composite paradigm, we asked participants to remember either an elastic moving face (i.e., a face that blinks and chews) or a static face, and then tested with a static composite face. The composite effect was (a) significantly smaller in the dynamic condition than in the static condition, (b) consistently found with different face encoding times (Experiments 1-3), and (c) present for the recognition of both upper and lower face parts (Experiment 4). These results suggest that elastic facial motion facilitates part-based processing rather than holistic processing. Thus, whereas previous work with static faces has emphasized an important role for holistic processing, the current work highlights an important role for featural processing with moving faces.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".