Perceptual-cognitive training improves biological motion perception
Bibliographic record
Abstract
In our everyday life, processing complex dynamic scenes such as crowds and traffic is of critical importance. Further, it is well documented that there is an age-related decline in complex perceptual-cognitive processing, which can be reversed with training. It has been suggested that a specific dynamic scene perceptual-cognitive training procedure [the three-dimensional multiple object tracking speed task (3D-MOT)] helps observers manage socially relevant stimuli such as human body movements as seen in crowds or during sports activities. Here, we test this assertion by assessing whether training older observers on 3D-MOT can improve biological motion (BM) perception. Research has shown that healthy older adults require more distance in virtual space between themselves and a point-light walker to integrate BM information than younger adults. Their performances decreased markedly at a distance as far away as 4 m (critical for collision avoidance), whereas performance in young adults remained constant up to 1 m. We trained observers between 64 and 73 years of age on the 3D-MOT speed task and looked at BM perception at 4 and 16 m distances in virtual space. We also had a control group trained on a visual task and a third group without training. The perceptual-cognitive training eliminated the difference in BM perception between 4 and 16 m after only a few weeks, whereas the two control groups showed no transfer. This demonstrates that 3D-MOT training could be a good generic process for helping certain observers deal with socially relevant dynamic scenes.
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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".