Learning to Ensemble? Repeated exposure leads to more efficient processing of summary statistics for weight and real-world size
Bibliographic record
Abstract
Ensemble coding is the brain’s ability to rapidly extract summary statistics from groups of similar items (e.g., average colour of leaves on a tree). Ensemble coding has also been found for information that cannot be gleaned solely from retinal input (e.g., average animacy of objects). We extended this line of ensemble coding research by examining if observers were sensitive to two different features that require access to stored information in long-term memory. Specifically, participants made judgements about the average weight or real-world size of groups of objects. We found that participants were unable to integrate information from multiple items to produce accurate summary statistics for either judgement. Next, we examined how learning and memory may influence ensemble coding for weight and real-world size. Could associative learning (e.g., learning which items belong in an ensemble via their relationship to other items) aid in ensemble coding? Specifically, we examined if repeated exposures to the different items within an ensemble would enable people to form summary statistics more efficiently. To test this, participants made judgements about the average weight or average real-world size of groups of object stimuli, and we manipulated how frequently people were exposed to certain subsets and combinations of the stimuli in a given ensemble. We found that repeated presentation of the ensemble stimuli leads to improved performance on the ensemble tasks for both the weight and size judgements. In summary, while observers were unable to extract average weight and real-world size information from objects when stimuli were not repeatedly presented, they were able to produce accurate summary statistics with multiple stimulus exposures. We speculate that this may be due to mechanisms governing associative statistical learning and memory for repeatedly encountered visual information.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".