Visual interference can help and hinder memory: Capturing representational detail using the Validated Circular Shape Space
Bibliographic record
Abstract
Abstract Although we can all agree that interference induces forgetting, there is surprisingly little consensus regarding what type of interference most likely disrupts memory. We previously proposed that the similarity of interference differentially impacts the representational detail of color memory. Here, we extend this work by applying the Validated Circular Shape Space (Li et al., 2020) for the first time to a continuous retrieval task, in which we quantified both the visual similarity of distracting information as well as the representational detail of shape memory. We found that the representational detail of memory was systematically and differentially altered by the similarity of distracting information. Dissimilar distractors disrupted both fine- and coarse-grained information about the target, akin to memory erasure. In contrast, similar distractors disrupted fine-grained target information but increased reliance on coarse-grained information about the target, akin to memory blurring. Notably, these effects were consistent across two mixture models that each implemented a different scaling metric (either angular distance or perceived target similarity), as well as a parameter-free analysis that did not fit the mixture model. These findings suggest that similar distractors will help memory in cases where coarse-grained information is sufficient to identify the target. In other cases where precise fine-grained information is needed to identify the target, similar distractors will impair memory. As these effects have now been observed across both stimulus domains of shape and color, and were robust across multiple scaling metrics and methods of analyses, we suggest that these results provide a general set of principles governing how the nature of interference impacts forgetting.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".