Testing the flexibility of ensemble coding: Limitations in cross-modal ensemble perception.
Bibliographic record
Abstract
Ensemble coding (the brain's ability to rapidly extract summary statistics from groups of items) has been demonstrated across a range of low-level (e.g., average color) to high-level (e.g., average facial expression) visual features, and even on information that cannot be gleaned solely from retinal input (e.g., object lifelikeness). There is also evidence that ensemble coding can interact with other cognitive systems such as long-term memory (LTM), as observers are able to derive the average cost of items. We extended this line of research to examine if different sensory modalities can interact during ensemble coding. Participants made judgments about the average sweetness of groups of different visually presented foods. We found that, when viewed simultaneously, observers were limited in the number of items they could incorporate into their cross-modal ensemble percepts. We speculate that this capacity limit is caused by the cross-modal translation of visual percepts into taste representations stored in LTM. This was supported by findings that (a) participants could use similar stimuli to form capacity-unlimited ensemble representations of average screen size and (b) participants could extract the average sweetness of displays when items were viewed in sequence, with no capacity limitation (suggesting that spatial attention constrains the number of necessary visual cues an observer can integrate in a given moment to trigger cross-modal retrieval of taste). Together, the results of our study demonstrate that there are limits to the flexibility of ensemble coding, especially when multiple cognitive systems need to interact to compress sensory information into an ensemble representation. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".