Biological motion targets have to be further away in virtual space for older versus younger adults to maintain good performance
Bibliographic record
Abstract
Human ability to perceive biological motion pattern is well established. Furthermore, it has been shown that older observers can be quite efficient at detecting biological motion. Recently, Legault & Faubert (VSS 2008) showed that young adult biological motion perception is influenced by distance in virtual space. Observers obtained good performance when a 1.8 meter biological motion target was located 1 meter or further but performances decreased dramatically at the shorter distance (less than a meter). The purpose of the present study was to determine if there is a difference between younger and older adult's performances when biological motion patterns are presented at different distances in virtual space. To create our setup, we used a full immersive virtual reality environment (CAVE), giving the observers an immersive 3D experience. We used a biological motion pattern composed of 13 dots, walking left or right on a treadmill. The size of the walker was 1.80 meters and it was shown at virtual distances from the observer of 0.50, 1, 2, 4 and 16 meters. The observer's task was to identify the walker's direction (left or right) in upright and inverted conditions. The walker was presented in a scrambled mask which was generated by randomly selecting dots with biological motion patterns and repositioning them in 3D space. Threshold mask density was determined using an adaptive staircase procedure. The results showed that older adults are influence by distance and that their performance begins to decrease at a distance of 4 meters, compared to young adults who perform well down to a distance of 1 meter. In other words, biological motion detection in noise, in upright and inverted conditions, depends on how far the walker is positioned in 3D virtual space and the critical distance where biological motion judgements break down highly depends on observer age.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".