Manipulating sensory information: Obstacle clearance strategies between typically developing children and adults
Bibliographic record
Abstract
The purpose of this study was to compare the effects of manipulating visual and somatosensory information during a multiple obstacle clearance task between children and adults. It was hypothesized that compared to adults, children would have difficulty with motor planning and online control during a multiple obstacle crossing task when sensory information was manipulated. Children (N=16,x ?=9+-1.07years) and adults (N=16,x ?= 22+-0.96years) walked along a 7m pathway towards a goal while avoiding stepping on one, or two obstacles. Visual information regarding the number of obstacles was either presented at the start of locomotion, or two steps prior to the first obstacle. Each participant completed thirty-six trials, 18 on flat ground and 18 on foam terrain. Full body kinematic data was collected using the NDI Optotrak motion analysis system (60Hz). Lead and Trail limb foot position variability were determined relative to the first obstacle. For Lead foot variability, there was an interaction between obstacle appearance, type of terrain, and age group, such that children were more variable on foam when obstacle appearance was delayed (F(1,29)=6.16,p=0.02). For Trail foot variability, there was an interaction between the amount of visual information provided and age group (F(1,29)=10.55,p=.003). Children were more variable when obstacle appearance was delayed compared to adults. The present study found that children have difficulty with online control of locomotion and demonstrate immature motor planning strategies. Children use visual information in a feedforward manner, rather than an online control, resulting in high variability in obstacle clearance behaviours when unexpected obstacles need to be avoided.Acknowledgments: NSERC
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".