Independence of viewpoint and identity in face ensemble processing
Bibliographic record
Abstract
Ensemble encoding refers to the visual system's ability to extract a summary representation from multiple items in a set-such as the mean identity of faces in a crowd-circumventing capacity limitations in visual working memory. In the present study we investigated face ensemble representations of higher level identity and lower level viewpoint with the aim of elucidating the extent of their overlap or independence. To this end, we used ensemble displays consisting of six face stimuli which could vary in identity, viewpoint, or both. Across three experiments, participants were asked to report an average identity, a single identity, an average viewpoint, or a single viewpoint, as cued by a central probe face following a stimulus display. In Experiment 1, we observed a dissociation between the processing of identity and viewpoint, as well as between average- and single-viewpoint extraction. Experiment 2 showed viewpoint-invariant identity processing across a wide range of mean viewpoints, spanning 120°. In Experiment 3, accuracy in reporting a response-relevant attribute was unaffected by changes in an irrelevant attribute. Participants were also capable of extracting both attributes simultaneously with little change in accuracy. Taken together, these results argue for the independence of identity and viewpoint in face ensemble processing.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".