Reclassifying guesses to increase signal-to-noise ratio in psychological experiments
Bibliographic record
Abstract
This paper introduces a novel procedure that can increase signal-to-noise ratio in psychological experiments that use accuracy as a selection variable for another dependent variable. This procedure relies on the fact that some correct responses result from guesses and reclassifies them as incorrect responses using a trial-by-trial reclassification evidence such as response time. It selects the optimal reclassification evidence criterion beyond which correct responses should be reclassified as incorrect responses. We show that the more difficult the task and the fewer the response alternatives, the more to be gained from this reclassification procedure. We illustrate the procedure on behavioral and ERP data from two different datasets (Caplette et al., 2020; Faghel- Soubeyrand et al., 2019) using response time as reclassification evidence. The reclassification procedure increased signal-to-noise ratio by 13-20%. Matlab and Python implementations of the reclassification procedure are freely available (https://github.com/GroupeLaboGosselin/Reclassification).
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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 | 0.034 | 0.210 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".