A response reclassification procedure to reduce noise caused by guesses
Bibliographic record
Abstract
Researchers studying cognition often rely on behavioral measures to uncover the underlying process behind correct and incorrect categorizations. However, these behavioral measures do not usually take into account the correct responses that are simply due to chance, which occurs when subjects guess. In a two-alternative discrimination task with a 75% correct response rate, for example, as much as 25% of all responses (or a third of all correct responses) are falsely correct. Here, we present a simple response reclassification procedure that reduces noise caused by false correct responses using response times (RT). The procedure determines, from the observed correct and incorrect RT distributions, a RT cutoff above which correct responses are relabeled as incorrect responses. To illustrate the procedure, we used two published datasets (Faghel-Soubeyrand & Gosselin, 2019; Royer et al., 2015) that employed Bubbles, a method relying heavily on response accuracy to reveal the information used to resolve a visual task. The standard weighted-sum computation applied to the reclassified accuracies led to a 15-20% increase in signal-to-noise ratio—equivalent to running between 32-44% more subjects—compared to the same computation applied to the recorded accuracies. A Matlab implementation of this reclassification procedure is freely available.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".