Beyond Traditional Assessments of Cognitive Status: Exploring the Potential of Spatial Navigation Tasks
Bibliographic record
Abstract
Abstract Deficits in spatial and navigation abilities are among the earliest signs of dementia. Yet, traditional neuropsychological tests primarily target memory and attention. The Spatial Performance Assessment for Cognitive Evaluation (SPACE) is a novel gamified digital assessment for iPads that uses various spatial tasks to detect early deficits in spatial navigation abilities indicative of cognitive impairment. In this study, 348 participants aged 21–76 completed the Montreal Cognitive Assessment (MoCA), SPACE, and a sociodemographic and health questionnaire. We investigated whether SPACE could predict scores on the MoCA beyond known risk factors for cognitive impairment. Using a factor analysis, we then assessed whether SPACE could complement the MoCA by capturing latent variables independent of MoCA scores that represent additional spatial aspects of cognitive functioning. Results from a hierarchical regression revealed that the pointing and perspective taking tasks in SPACE significantly predicted MoCA scores beyond age and gender. Surprisingly, none of the risk factors predicted MoCA scores. The factor analysis revealed that the MoCA and perspective taking contributed to a separate factor from other navigation tasks in SPACE. We further provide normative data for age and gender for each task in SPACE, which can serve as benchmarks in future studies to identify individuals at risk.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".