Distinct contributions of explicit and implicit memory processes to weight prediction when lifting objects and judging their weights: an aging study
Bibliographic record
Abstract
Weight predictions used to scale lifting forces adapt quickly when repeatedly lifting unusually weighted objects and are readily updated by explicit information provided about weight. In contrast, weight predictions used when making perceptual judgments about weight are more resistant to change and are largely unaffected by explicit information about weight. These observations suggest that distinct memory systems underlie weight prediction when lifting objects and judging their weights. Here we examined whether these weight predictions differ in their reliance on declarative and nondeclarative memory resources by comparing the adaptability of these predictions in older adults, who exhibit relatively impaired declarative memory processes, to those in younger adults. In the size condition, we measured lift forces as participants repeatedly lifted a pair of size-weight inverted objects in alternation. To assess weight judgments, we measured the size-weight illusion every 10 lifts. The material condition was similar, except that we used material-weight inverted objects and measured the material-weight illusion. The strengths of these illusions prior to lifting, and the attenuation of the illusions that arise when lifting inverted objects, were similar for both groups. The magnitude of the change in the illusions was positively correlated with implicit memory performance in both groups, suggesting that predictions used when judging weight rely on nondeclarative memory resources. Updating of lifting forces also did not differ between groups. However, within the older group the success with which lifting forces were updated was positively correlated with working memory performance, suggesting that weight predictions used when lifting rely on declarative memory resources.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".