Semantic partitioning facilitates memory for object location through category-partition cueing
Bibliographic record
Abstract
In our lived environments, objects are often semantically organised (e.g., cookware and cutlery are placed close together in the kitchen). Across four experiments, we examined how semantic partitions (that group same-category objects in space) influenced memory for object locations. Participants learned the locations of items in a semantically partitioned display (where each partition contained objects from a single category) as well as a purely visually partitioned display (where each partition contained a scrambled assortment of objects from different categories). Semantic partitions significantly improved location memory accuracy compared to the scrambled display. However, when the correct partition was cued (highlighted) to participants during recall, performance on the semantically partitioned display was similar to the scrambled display. These results suggest that semantic partitions largely benefit memory for location by enhancing the ability to use the given category as a cue for a visually partitioned area (e.g., toys - top left). Our results demonstrate that semantically structured spaces help location memory across partitions, but not items within a partition, providing new insights into the interaction between meaning and memory.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".