Modeling Dual-Task Performance: Identifying Key Predictors Using Artificial Neural Networks
Bibliographic record
Abstract
Dual-task paradigms that combine cognitive and motor tasks offer a valuable lens for detecting subtle impairments in cognitive and physical functioning, especially in older adults. This study used artificial neural network (ANN) modeling to predict clinical, cognitive, and psychosocial outcomes from integrated gait, speech-linguistic, demographic, physiological, and psychological data collected during single- and dual-task conditions. Forty healthy adults (ages 20-84) completed physical, cognitive, and psychosocial assessments and a dual-task walking task involving cell phone use. ANN models were optimized using hyperparameter tuning and k-fold cross-validation to predict outcomes such as the Montreal Cognitive Assessment (MOCA), Trail Making Tests (TMT A and B), Activities-Specific Balance Confidence (ABC) Scale, Geriatric Depression Scale (GDS), and measures of memory, affect, and social support. The models achieved high accuracy for MOCA (100%), ABC (80%), memory function (80%), and social support satisfaction (75%). Feature importance analyses revealed key predictors such as speech-linguistic markers and sensory impairments. First-person plural pronoun used and authenticity of internal thoughts during dual-task emerged as strong predictors of MOCA and memory. Models were less accurate for complex executive tasks like TMT A and B. These findings support the potential of ANN models for the early detection of cognitive and psychosocial changes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".