Tuning the ensemble: Incidental skewing of the perceptual average through memory-driven selection.
Bibliographic record
Abstract
The process by which multiple items within an object grouping are rapidly summarized along a given visual dimension into a single mean value (i.e., perceptual averaging) has increasingly been shown to interact dynamically with visual working memory (VWM). Commonly, this interaction is studied with respect to the influence of perceptual averaging over VWM, but it is also the case that VWM can support perceptual averaging. Here, we argue that, in the presence of memory-matching elements, VWM exerts an obligatory influence over perceptual averaging even when it is detrimental to do so. Over four experiments, we tested our hypothesis by having individuals perform a mean orientation estimation task while concurrently maintaining a colored object in VWM. We anticipated that mean orientation reports would be attracted to the local mean of memory-matching items if such items are prioritized in perceptual average judgments. This was indeed the case as we observed a persistent bias in mean orientation judgments toward the subset mean of items matching the VWM item color, despite color being entirely irrelevant to the mean orientation task. Our results thus highlight a goal-invariant influence of VWM over perceptual averaging, which we attribute to amplification through memory-driven selection. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".