The influence of postural threat on strategy selection in a stepping-down paradigm
Bibliographic record
Abstract
To walk safely in their environment, people need to select adequate movement strategies during gait. In situations that are perceived as more threatening, older adults adopt more cautious strategies. For individuals with excessive fear, selecting adequate strategies might be troubling. We investigated how a postural threat affects the selection of strategies within and between older adults by using a stepping-down paradigm. In twenty-four older adults we determined the height at which they switched in stepping-down strategies from a less demanding but more balance threatening heel landing to a more demanding yet safer toe landing. We expected that this switching height would be lower in the high (0.78 m elevation) compared to low threat (floor level) condition. Furthermore, we investigated if older adults, for which the postural threat evoked an increase in the perceived fear, presented a different stepping down strategy due to the postural threat. Our results indicated that the postural threat changed older adults' strategies selection towards a more conservative toe landing. Hence, despite the additional effort, older adults prefer more cautious strategies during a postural threat. No effects of perceived fear on strategy selection between individuals were observed, potentially due to relatively small differences in fear among participants.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".