Ensemble Scene Processing is Regulated by Feature Complexity
Bibliographic record
Abstract
Past literature has suggested that summary statistics for groups (i.e., ensembles) of faces or objects can be rapidly extracted, with visual ensemble perception typically becoming more efficient as set size increases. Tharmaratnam and colleagues (VSS 2019) recently demonstrated that the average scene content (i.e., perceived naturalness or manufacturedness) and spatial boundary (i.e., perceived openness or closedness) of scene ensembles can be extracted without reliance on visual working memory (VWM) resources. However unlike past literature, the task difficulty of extracting average scene ensemble features increased with increasing set sizes. To investigate this further, in the present study we varied scene ensemble task difficulty by manipulating scene feature complexity. In Experiment 1, participants were asked to report the average orientation of ensembles of rotated scenes (simpler feature). In Experiment 2, participants were asked to report the average sound level of ensembles of scenes that varied in perceived sound quality (i.e., noisy or quiet; complex feature). In both experiments, we varied set size by randomly presenting 1, 2, 4, or 6 scenes to participants on each trial, and additionally measured VWM capacity using a two-alternative forced-choice task. We found that participants were able to accurately extract summary statistics for both ensemble scene features. Importantly, all 6 items were integrated into their percepts without relying on VWM, as less than 1.3 scenes were remembered on average. Interestingly, when set-size increased, task performance did not change when rating average scene orientation, but decreased when rating average sound level. This latter finding is consistent with our previous findings measuring average scene content and spatial boundary. Taken together, these results broaden our understanding of the cognitive mechanisms governing ensemble perception by demonstrating that the number of items and the feature complexity of the incoming sensory information both contribute to the formation of ensemble representations.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".