MULTI-MODAL TRAINING TO IMPROVE COGNITION, MOBILITY, AND BRAIN FUNCTIONING IN OLDER ADULTS
Bibliographic record
Abstract
A growing body of research argues for cross-over effects of training, such that exercise training leads to improved cognitive abilities and more efficient neural functioning (Bherer, Erickson, & Liu-Ambrose. 2013; Li, Yao, Cheng, et al., 2016). In parallel, computerized cognitive training has led to improved balance and mobility (Li, Roudaia, Lussier, et al., 2010). What underlies these cross-over effects may be the common cognitive functions and brain regions or networks that are jointly associated with cognitive and motor control. However, fewer studies have examined the potential synergistic effects of multi-modal training in the form of mixed cognitive and physical training schedules, virtual reality, computer gaming, or dual-task training (Basak, Boot, Voss, & Kramer, 2008; Mirelman, Maidan, Herman et al., 2011). This symposium presents recent work on this emerging topic, spanning a variety of approaches, such as gaming (Basak), virtual reality (Hasudorff), and combined exercise and cognitive training (Bherer, K. Li, C. Li). We will highlight a variety of outcomes measures including cognitive, motoric, and neural indices. We will discuss the influence of training format, task coherence, and trainee enjoyment and motivation on the magnitude of training-related gains. We will also discuss the specificity of training-related effects.
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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 | 0.000 | 0.001 |
| 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".