The Role of Visual Working Memory in Capacity-Limited Cross-Modal Ensemble Coding
Bibliographic record
Abstract
Ensemble coding (the brain’s ability to rapidly extract summary statistics, such as average size, from groups of items) aids observers in circumventing the capacity limitation of visual working memory (VWM). Despite this, there are bi-directional interactions between the two systems, although it is unclear if there are cases when VWM may directly support ensemble coding (e.g., generating a summary statistic from information held within VWM). Although ensemble coding is generally viewed as being able to circumvent item capacity limits, capacity limits are observed in the ensemble coding literature. Recently, we encountered a capacity limit (i.e., integration of only four out of six items) in a cross-modal ensemble task where observers were asked to extract the average taste (sweetness) of groups of food pictures. In the present study, we investigated this finding further by examining the role of VWM in explaining the observed capacity limitation. To test this, observers performed both an ensemble task and a VWM task, and we determined 1) how much information they integrated into their cross-modal ensemble percepts, and 2) how much information they remembered from those displays. Across two experiments, we found that individual differences in VWM capacity did not explain differences in performance on the ensemble coding task (i.e., high-capacity individuals did not have significantly higher “ensemble abilities” than low-capacity individuals). While our data cannot definitively state whether or not VWM is necessary to perform the ensemble task, we conclude that it is certainly not sufficient to support this cognitive process. We speculate that the capacity limit may be explained by 1) a bottleneck at the perceptual stage (i.e., a failure to process multiple visual features across multiple items, as there are no singular features that betray taste), or 2) the interaction of multiple cognitive systems (e.g., VWM, gustatory working memory, long term memory). Our results highlight the importance of examining ensemble perception across multiple sensory and cognitive domains to provide a clearer picture of the mechanisms underlying everyday behavior.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".