The effect of aging and cognitive decline on spatial and temporal cognition
Bibliographic record
Abstract
Alzheimer's disease (AD) is one of the most challenging health conditions in our century. While there is yet no cure for this degenerative disease, the earlier it is diagnosed and treated, the more effective the treatment could be. Studies show AD-related neuro-pathological changes occur years before detectable clinical symptoms appear. Therefore, a number of computer-based cognitive tests have been designed to measure different cognitive abilities such as working memory or associative memory in older adults. However, the early effects of dementia on particular aspects of spatial and temporal cognition, such as spatial encoding/updating and explicit time perception, has not received similar attention. We hypothesized that spatial encoding/updating and explicit timing are among the early symptoms of the onset of AD and can provide reliable and accurate measures for detecting the onset of cognitive decline. Thus, we designed and conducted several Virtual Reality experiments to assess human spatial encoding, spatial updating and explicit timing in different aging groups. Two new accuracy-based measures were also introduced in this work: error score for assessing spatial orientation and signed error for assessing explicit timing. The significant correlations between the participants’ performance and their age and cognitive scores supported the validity of the designed measures. The conducted experiments revealed significant differences between the performances of younger and older adults, and between high- and low-cognitive functioning participants in spatial encoding, spatial updating and explicit timing tests. These results encourage development of predictive models for differentiating between cognitively-intact and cognitively declined older adults based on their performance in the spatial and temporal tests.
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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 | 0.003 |
| 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 | 0.000 | 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".