The impact of mnemonic interference on memory for visual form
Bibliographic record
Abstract
How does interference impact memory? Previous work found that different types of distracting information can differentially alter how visual representations are forgotten. In addition, a recent series of experiments found that highly dissimilar interfering items erase the contents of memory, while highly similar and variable interfering items blur memory representations. Though these effects have been shown for color memory, it is unclear if they extend to other object features such as shape. Here, we used a novel "Shape Wheel" to assess how different kinds of interference would impact shape memory. On this wheel, 2D line drawings were morphed together to create an array of 360 shapes, corresponding to 360 degrees on a circle. Participants were asked to remember a shape sampled from this wheel, then were shown interfering shapes that were either perceptually similar to the studied shape, perceptually dissimilar from the studied shape, perceptually variable, or scrambled shapes (baseline condition). We used a mixture model to measure the probability that the item is stored in memory, defined as accuracy, as well as the level of detail of that representation, defined as precision. We found that when interfering shapes were similar to the studied item, a numerical but non-significant benefit to memory accuracy was observed. However, when interfering shapes were dissimilar to the studied item, accuracy was reduced. In contrast, memory precision was reduced only when interfering shapes were similar or perceptually variable. These findings extend previous results by demonstrating the differential effects of interference for isolated feature-level shape information. Visually dissimilar interference erases memory representations, while visually variable and highly similar interfering items tended to blur shape representations. As the impact of interference was consistent across studies, these findings may offer a set of general principles regarding how interference impacts high-level object representations and all features therein. Meeting abstract presented at VSS 2017
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".