Integration versus separation in Stroop‐like counting interference tasks<sup>1</sup>
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Bibliographic record
Abstract
Abstract: Two versions of a Stroop‐like counting interference task were compared to examine how irrelevant information is ignored. In the integrated task, participants enumerated digits (inconsistent condition) or letters (neutral condition) while attempting to ignore the identity of the characters. In the newly created separated task, participants enumerated asterisks while attempting to ignore a single digit (inconsistent condition) or letter (neutral condition) at fixation. Interference (longer responses in the inconsistent condition than in the neutral condition) was small but significant in both tasks but was not reduced by separation. Contrasted with the pattern in the color–word Stroop task, these results suggest that dimensional separation has different effects on interference depending on the overall amount of interference.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it