Spatial Abilities and Aging: A Meta-Analysis
Bibliographic record
Abstract
UNLABELLED: BACKGROUND/STUDY CONTEXT: Age-related effects in performance on spatial tasks have been well documented, with results suggesting a negative effect on performance in older samples. Although meta-analyses have been conducted examining performance on specific spatial tasks, it appears that data incorporating a variety of tasks have not yet been integrated into a single meta-analyses. METHODS: The present study examined age-related effects on spatial abilities in a multilevel meta-analysis of 137 effect sizes, drawn from 80 samples dated between 1958 and 2011. In addition to sample characteristics (education, year of publication, and age range), procedural factors (spatial ability category, spatial task, dependent variable, task setting, and medium of administration) were also considered. The standardized mean difference (Cohen's d) was used as the effect size measure in meta-analytic calculations. RESULTS: RESULTS revealed a large (mean d = 1.01) age-related decrease in spatial performance on psychometric tests. Specifically, older adults (mean age range = 63-79.5 years) performed worse on psychometric tests than younger adults (mean age range = 17-28.6 years). Interestingly, this age effect was unaffected by factors such as specific test, test category (mental rotation, spatial perception, or spatial visualization), timing conditions, and group or individual administration. However, measures of response time produced significantly larger effects of age than measures of accuracy on spatial performance. CONCLUSION: The present analysis demonstrates a clear pattern of negative age effects in spatial ability across the literature. Although these effects are unaffected by the specific spatial component under investigation or testing conditions, speed of processing was shown to be an important factor in spatial performance. The need to report more thoroughly on characteristics of young and old participants in future studies is also emphasized.
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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 | 0.017 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.043 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".