Bibliographic record
Abstract
How do we form mental links between related items? Forming associations between representations is a key feature of episodic memory and provides the foundation for learning and guiding behavior. Theories suggest that spatial context plays a supportive role in episodic memory, providing a scaffold on which to form associations, but this has mostly been tested in the context of autobiographical memory. We examined the memory boosting effect of spatial stimuli in memory using an associative inference paradigm combined with eye-tracking. Across two experiments, we found that memory was better for associations that included scenes, even indirectly, compared to objects and faces. Eye-tracking measures indicated that these effects may be partly mediated by greater fixations to scenes compared to objects, but did not explain the differences between scenes and faces. These results suggest that scenes facilitate associative memory and integration across memories, demonstrating evidence in support of theories of scenes as a spatial scaffold for episodic memory. A shared spatial context may promote learning and could potentially be leveraged to improve learning and memory in educational settings or for memory-impaired populations.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".