Brain training: rationale, methods, and pilot data for a specific visuomotor/visuospatial activity program to change progressive cognitive decline
Bibliographic record
Abstract
INTRODUCTION: Research in the field of the aging brain has evolved to the extent that it is now commonly understood that actively engaging in cognitive tasks provides the potential of being beneficial in affecting the trajectory of age-related cognitive decline. What remains to be examined is the extent, and type, of program required to effect change in aging cognitively impaired individuals. METHODS: To address this issue, a cognitive program focusing on the use of visuospatial (VS)/visuomotor (VM) elements was applied to a group of six older individuals with identified progressive cognitive impairments. It was hypothesized that using tasks with VS and VM components may be beneficial in supporting overall brain performance, and subsequently assist individuals to perform well in various cognitive and behavioral tasks. RESULTS: Results showed that on many evaluative measures individuals remained stable, or improved in performance with medium-to-large effect sizes (e.g., 0.3-1.0). Thus, in a cognitively impaired population sample where decline would be the norm, our participants improved or remained stable. CONCLUSION: The novel application of a VS/VM training program shows promise in addressing global cognitive decline, by targeting a brain area susceptible to early disruptions and providing it with additional and ongoing stimulative tasks in an effort to bolster its functioning and subsequently overall brain functioning.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".