Exploring the effects of visual cue complexity on foot placement accuracy in a targeted stepping task
Bibliographic record
Abstract
Locomotion requires some degree of cognitive demand and involvement of executive functions including inhibition, updating, and task switching. Secondary tasks that require cognitive demand can impact our walking performance. Online visual information and executive functions coordinate how we navigate and progress safely through our environment, for example, avoiding ice in the winter or stopping at a cross walk. This study explored how inhibition, updating, and task switching are used during a visually guided targeted stepping and obstacle avoidance task. Participants walked along a straight walkway and stepped on or over a colour changing rectangular obstacle (42x20x5cm). The obstacle would change from white to red or green during approach. Instructions given to the participant coupled with the light change would result in a specific response: Preliminary instructions (green=on, red=over); Switched Instructions (green=over, red=on). It was expected that the switched instructions would pose an increased cognitive demand and subsequently result in a decrease in performance. The following gait measures were analyzed 1) Foot placement accuracy 2) maximum head tilt angle 3) Center of mass (COM) velocity. To date, our preliminary data for young adults (N=6) have shown that foot placement was most accurate in medial lateral direction (<1cm error) compared to anterior-posterior (AP). In the AP direction, foot placement accuracy was highest for the preliminary instructions with green lights (on) and least accurate in the switched instructions with green lights (over). This suggests that with increased cognitive demand, there is a decrease in motor accuracy. Data analyses and collection are ongoing. These findings will help further our understanding about the impact of complex visual cues on targeted stepping and obstacle avoidance tasks.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".