The effects of a human confederate and goal location on the path selection of young adults
Bibliographic record
Abstract
INTRODUCTION: The behavioural dynamics model suggests that obstacles correspond to repellers forcing trajectories to diverge, whereas goals act as attractors in which path trajectory converges. However, environmental obstacles are avoided based on characteristics; path trajectories are wider for animate compared to inanimate objects. Nonetheless, the effects of different obstacle properties (animate vs. inanimate) on repulsion forces are unclear. The purpose of this study was to determine whether the attraction of a goal dominates path selection despite obstacle characteristics. METHODS: Participants (N=15) were instrumented with IRED markers (NDI Optotrak) on the head and trunk to calculate the location of the Centre of Mass (COM) over time. Participants were instructed to walk along a 10m path toward a goal located at one of three possible locations: 1) midline of the pathway; 2) 80cm to left of midline; or 3) 80cm to right of midline. Halfway along the pathway three obstacles were placed perpendicular to the pathway consisting of either three vertical poles (20cm diameter) or two vertical poles and a confederate. The obstacles were each separated by 80cm, creating two equal apertures on either side of the midline. The location of the confederate was either: 1) along midline; 2) 80cm to left of midline; 80cm to right of midline; or 4) not present. RESULTS: A stepwise multiple regression analysis was used to test if gender, shoulder width, confederate location, and goal location significantly predicted participants' path selection (i.e., medial-lateral position of COM at time of crossing the obstacles). The results of the regression indicated that goal location explained 48.9% of the variance (R2=.489, F(1,175)=170.6,p< .001) in path selection (β = .700, p< .001). CONCLUSION: The findings suggest that different obstacle characteristics (animate vs. inanimate) do not affect repulsion but rather the attraction of the goal is what dominates one's path. Meeting abstract presented at VSS 2016
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".