Bibliographic record
Abstract
The visual system is remarkably efficient at extracting statistical ensembles from objects in the environment, such as the mean size or orientation. Yet at any given time, multiple groups of objects can be randomly distributed over space. Thus, the challenge for the visual system is to summarize over multiple intermixed sets at once. What is the limit of the ability to perceive multiple ensembles? In a series of experiments, participants viewed an array of 1 to 8 spatially intermixed sets of circles for 1000ms in each trial. Each set contained four circles in the same colors but with different sizes. A probed set was randomly chosen from the array, and was either pre-cued or post-cued. Participants estimated the mean size of the probed set. Fitting a uniform-normal mixture model to the error distribution for each number of set, we found a four-set limit of ensemble perception: observers could reliably estimate the mean size of circles from maximally four sets (Experiment 1). Importantly, their performance was unlikely to be driven by a subsampling strategy (Experiment 2). By extending exposure durations to 1500ms and 2000ms, we found that the estimation of mean size might be constrained by internal capacity constraints even when the constraint from processing speed was eliminated (Experiment 3). In addition, ensemble perception may be limited by the storage capacity of visual working memory (Experiment 4). Finally, the capacity for ensemble perception does not seem to be influenced by the imprecise representations of individual circles in the set (Experiment 5). Overall, our findings suggest that ensemble perception can operate over multiple intermixed sets, but with a four-set capacity limit. This result converges with previously observed limits in visual working memory, attention, and enumeration. The convergence implies that different forms of visual processes may share a common capacity constraint. Meeting abstract presented at VSS 2016
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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 | 0.002 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".