Current accuracy is not always the best predictor of future accuracy: The consequences of dissociable bias and precision components
Bibliographic record
Abstract
Skill acquisition scientists can rely on measures of bias, precision, and accuracy to describe and capture changes in action outcomes. Here we examined whether bias and precision are dissociable components of an individual’s accuracy. We first explored this by simulating two-dimensional accuracy at two timepoints and varied the persistence (i.e., “stickiness”) of bias and precision. When bias and precision were persistent, Time 1 accuracy was the strongest predictor of Time 2 accuracy. When precision was persistent and bias was not, Time 1 precision was the strongest predictor of Time 2 accuracy. If bias and precision are dissociable, then current accuracy is not always the best predictor of future accuracy. We then re-analyzed data from a previous experiment that likely included the above-described scenarios. The experiment involved participants tossing beanbags to an occluded target in pretest and retention without feedback and 50% relative feedback frequency in acquisition. Bias was random on the pretest due to the blind throw and no feedback. Consistent with simulations where precision was persistent and bias was not, pretest precision was a significantly better predictor of retention accuracy than pretest accuracy. However, after a couple blocks of receiving feedback, accuracy became the better predictor of future performance, consistent with simulations where both precision and bias are persistent. These results suggest bias and precision may be dissociable components of accuracy. Consequently, even if accuracy is the only measure of interest, including pretest precision rather than accuracy as a covariate may increase statistical power in similarly designed experiments.
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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 | 0.015 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".