Risky actions: Why and how to estimate variability in motor performance
Bibliographic record
Abstract
We describe the difficulties of measuring variability in performance, a critical but largely ignored problem in studies of risk perception. The problem seems intractable if a large number of successful and unsuccessful trials are infeasible. We offer a solution based on estimates of task-specific variability pooled across the sample. Using a dataset of adult performance in throwing and walking tasks, we show that mischaracterizing the slope leads to unacceptably large errors in estimates of performance levels that undermine analyses of risk perception. We introduce a “pooled-slope” solution that approximates estimates of individual variability in performance and outperforms arbitrary assumptions about performance variability within and across tasks. We discuss the advantages of objectively measuring performance based on the rate of successful attempts—modeled via psychometric functions—for improving comparisons of risk across participants, tasks, and studies. • Measures of performance variability are critical for measuring physical risk. • A small number of trials makes it difficult to estimate performance variability. • We introduce a new “pooled slope” method for estimating performance variability. • This method handles cases when data are insufficient for individual models.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".