Preserved Spatial Memory for Reaching to Remembered Three-Dimensional Targets in Aging
Bibliographic record
Abstract
UNLABELLED: BACKGROUND/STUDY CONTEXT: Compared with the large literature on the impact of aging on spatial memory span, far fewer studies have examined the influence of aging on spatial memory processes required to reach a remembered target. This study assessed the ability of seniors to accurately reach to three-dimensional (3D) memorized targets in four conditions in which the memory delay and the attentional demands varied. METHODS: The accuracy and variability of reaching movements (3D absolute, 3D variable, and spatial component errors) were analyzed to evaluate the performance of 12 young adults aged 20 to 30 and 12 older adults aged 62 to 69 in the different delay conditions (short passive delay, long passive delay, long cognitive delay, and long spatial delay). Variance analyses were applied on each error measure as well as on kinematic features of the movements (movement time, deceleration time, and peak velocity). RESULTS: Results revealed that older participants were as capable as their younger counterpart to maintain target location in memory regardless of task complexity. CONCLUSION: Although memory deficits have been found in older adults in several previous studies, the current results support the idea that healthy aging does not produce a breakdown in all memory tasks. Hence, a specific spatial memory channel seems to remain unaffected in normal aging.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".