Drawing the mind: assessing cognitive decline through self-figure drawings
Bibliographic record
Abstract
Background: Drawing requires the integration of visual perception, spatial processing, motor planning, and executive functions, but few studies have explored the potential connection between drawings, cognitive decline and dementia. Aim: This study compared self-figure drawings of elderly individuals with Alzheimer's disease (AD) and mild cognitive impairment (MCI) to those with normative cognitive functioning. Method: age = 73.97, 70% women) participated in this study. Participants completed the Montreal Cognitive Assessment (MoCA-5) and then engaged in a self-figure drawing task. The drawings were categorized into eight groups based on their graphic characteristics. MANCOVA was used to examine differences between the drawing groups, t-tests were used to examine cultural differences, and Chi-square tests were used to examine differences and associations between the drawing groups and the MoCA-5 scores or categories. Results: We found that normative cognitive performance was associated with adapted portraits, whereas moderate to severe impairment correlated with schematic, disorganized, and unusual portraits. Cultural differences were also observed: the Thai participants had higher MoCA-5 scores than their Israeli counterparts and fewer differences in drawing group distribution. Conclusion: These findings suggest that self-figure drawings may reflect the cognitive status of older adults, with more detailed and adapted drawings indicating better cognitive functioning. Implications for practice: Self-figure drawings can be used as a complementary tool for assessing cognitive decline in diverse populations. However, cultural differences in drawing styles and cognitive test performance underscore the need for culturally sensitive approaches to dementia assessment and research.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".