When a face is (or is not) more than the sum of its features: Configural and analytic processes in facial temporal integration
Bibliographic record
Abstract
To investigate temporal integration in face recognition, top and bottom halves of pictures of famous people were presented sequentially, either upright or inverted, with varying temporal intervals between the two halves. The inversion effect, a marker of configural processing, was comparable across 0–400 ms intervals, but decreased at intervals exceeding 400 ms (Exp. 1). When an interfering stimulus appeared during the interval between the two face parts (Exp. 2), it disrupted the integration of the parts but not their perception. This is the first report of such an effect. Thus, performance equalled the combined accuracy of each part when presented alone, which in turn was worse than when they were integrated. Our findings indicate that (a) configural processing of faces depends on integration of face parts that are maintained temporarily in a visual buffer; (b) without integration, identification depends on recognition of individual parts whose contributions are additive; and (c) an interfering visual stimulus can obstruct integration, but leaves perception of individual parts intact. The ability to integrate temporally separated face parts into a unified representation is discussed in light of theories of face perception and temporal integration.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".