3-Dimensional Multiple Object Tracking Training Can Enhance Selective Attention, Psychomotor Speed, and Cognitive Flexibility in Healthy Older Adults
Bibliographic record
Abstract
Objectives: The objective of this pilot study was to determine if a 3-dimensional multiple object tracking training (3D-MOT) intervention could improve performance on measures of attention, psychomotor speed, and cognitive flexibility in healthy older adults.Methods: Forty-six individuals aged 63-87 years old participated in the study.Twenty-five participants in the intervention group completed the Stroop task before and after intervention that consisted of seven training sessions with the Neurotracker, a 3D-MOT software program.Stroop test scores were examined for changes in selective attention, cognitive flexibility (CF), as well as psychomotor speed pre-and post-intervention.The 21 individuals in the control group completed the Stroop test at the pre-post interval, without completing the Neurotracker intervention.Results: The Neurotracker training intervention group showed significant improvements in both cognitive flexibility (M = 5.01, SE = 1.44, p = 0.002), and psychomotor speed and selective attention (M = 4.90, SE = 1.44, p = 0.002).Significant changes were also detected in a condition that measured psychomotor speed and cognitive flexibility together (M = 9.39, SE = 1.74, p < 0.001).No significant changes were detected in the control group. Conclusion:The current results suggest that the Neurotracker may be an effective tool for improving selective attention, cognitive flexibility, and psychomotor speed in healthy older individuals.
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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) | 0.001 | 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) | 0.001 | 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".