When a face is (or is not) more than the sum of its features: Configural and analytic processes in facial temporal integration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it