Enactment and Embodiment Impact the Recall of Object-Location Associations
Bibliographic record
Abstract
The role of self-generated movement in memory retrieval has been demonstrated in enactment paradigms. However, in the context of object-location memory, the impact of action during learning has not yet been investigated, despite the ecological relevance of such behaviors. In the current project, we present new evidence that actively placing an object in a target location during learning leads to more precise, and faster, subsequent recall of the object-location associations than simply observing this placement. We further demonstrate differences in object- location memory depending on the category of stimuli that participants are engaging with by showing that images of objects with high manipulability are placed more precisely, more quickly, and more directly (mouse-tracking) than images of objects with low manipulability. We suggest that these latter differences are due to the motor information implicitly activated during processing of high manipulability items, and reflect the embodied nature of concepts. Although both enactment and manipulability impacted object-location recall, they did not interact. This research extends findings on enactment to associative encoding processes, and informs our understanding of the relationship between enactment and embodiment in human 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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".