Competition increases binding errors in visual working memory
Bibliographic record
Abstract
When faced with maintaining multiple objects in visual working memory, item information must be bound to the correct object in order to be correctly recalled. Sometimes, however, binding errors occur, and participants report the feature (e.g., color) of an unprobed, non-target item. In the present study, we examine whether the configuration of sample stimuli affects the proportion of these binding errors. The results demonstrate that participants mistakenly report the identity of the unprobed item (i.e., they make a non-target response) when sample items are presented close together in space, suggesting that binding errors can increase independent of increases in memory load. Moreover, the proportion of these non-target responses is linearly related to the distance between sample items, suggesting that these errors are spatially specific. Finally, presenting sample items sequentially decreases non-target responses, suggesting that reducing competition between sample stimuli reduces the number of binding errors. Importantly, these effects all occurred without increases in the amount of error in the memory representation. These results suggest that competition during encoding can account for some of the binding errors made during VWM recall.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".