Attention and stimulus structure interact during ensemble encoding of facial expression
Bibliographic record
Abstract
Face ensemble encoding involves synthesizing summary information from groups of faces, providing a mechanism to overcome limitations in visual working memory. Yet, research on the role of attention has revealed mixed findings. Also, the simultaneous processing of summary representations and individual faces within an ensemble remains largely unexplored. Here, across three experiments, participants viewed ensembles without a central face (n = 32), or with a central face, while attention was distributed across the entire ensemble (n = 38) or focused centrally (n = 38). Critically, the consistency of center and surround faces varied as a function of emotional valence (i.e., same versus opposite). Participants completed an expression similarity-rating task between an ensemble and a single face, which was used to recover, via image reconstruction, visual estimates of summary representations. Reconstructions were then assessed against central faces, surrounding faces, and their averages. We show that focused attention enhances central face representation and that consistency benefits the representation of both the center and of the surround. However, central faces outweigh the overall surround representation only when attention is focused on a center face inconsistent with its surround. These findings reveal a flexible relationship between attention and stimulus structure in ensemble perception.
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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 | 0.000 | 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".