Examining the Impact of Item-Distractor Similarity Using a Validated Circular Shape Space
Bibliographic record
Abstract
How does interference impact memory? Previous work on memory for colour suggest that the similarity of distracting information can differentially alter how visual representations are forgotten. In Experiment 1, we describe the creation of the Validated Circular Shape Space (VCS space), a novel â Shape Wheelâ whereby the subjective similarity of 360 shapes varied incrementally around a circular space. Pairwise shape similarity ratings were collected over a series of validation steps and multidimensional scaling was used to iteratively validate the shape space to ensure subjective circularity. In Experiment 2, we then used this set of shapes to assess how shape memory was impacted by distracting information that varied in subjective similarity relative to the study shape. We found that when distractors were similar to the studied shape, a significant benefit to memory accuracy was observed. When distractors were dissimilar to the studied shape, accuracy was significantly reduced. In contrast, no differences in precision was observed in any of the conditions relative to baseline. These results may imply the existence of surround suppression which acts to shield shape memory from the degradation of highly similar distractors. Overall, we have developed a novel circular shape space that is homogenous across individuals and show that memory for shapes can be systematically impacted by the subjective similarity of distractors.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".