Training 3D-MOT improves biological motion perception in aging: evidence for transferability of training.
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 for complex perceptual-cognitive dynamic processes and that such processes can be trained, reversing aging effects (VSS 2011). It has been suggested that training for 3D-Multiple Object Tracking (3D-MOT) under certain conditions helps observers manage complex dynamic scenes in real life situations (Faubert & Sidebottom, 2011). Here we test this proposition by assessing whether training older observers on 3D-MOT can improve a socially relevant task such as biological motion perception. In complex scenes such as crowds, the perception of individual dynamics is important for society living. These human dynamics can be expressed by biological motion patterns. Previous research has shown that older adults require more distance in virtual space between themselves and the point-light walker to integrate biological motion information (VSS 2009). Older adults’ performances dramatically decrease at a distance as far away as 4 m (in zones where it gets critical for collision avoidance), whereas younger adults’ performance remains constant up to 1 m. We trained younger and older observers on the 3D MOT speed task and looked at younger and older adults’ performance on biological motion task presented at 4 and 16m distance in virtual space. We also trained a control group on a visual perceptual task in the same testing conditions. Results demonstrated that, while the control group condition showed no improvements, 3D-MOT training reversed age-related biological motion perception loss where the difference found for older adults between 4 and 16 m disappeared after a few weeks of training. This demonstrates that 3D-MOT training in aging could be a good generic process for helping older observers deal with complex dynamic scenes such as when driving or navigating in dense crowds. Meeting abstract presented at VSS 2012
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".