Does Posture Influence the Stroop Effect?
Bibliographic record
Abstract
Rosenbaum, Mama, and Algom (2017) reported that participants who completed the Stroop task (i.e., name the hue of a color word when the hue and word meaning are congruent or incongruent) showed a smaller Stroop effect (i.e., the difference in response times between congruent and incongruent trials) when they performed the task standing than when sitting. We report five attempted replications (analyzed sample sizes: N = 108, N = 108, N = 98, N = 78, and N = 51, respectively) of Rosenbaum et al.’s findings, which were conducted in two institutions. All experiments yielded the standard Stroop effect, but we failed to detect any consistent effect of posture (sitting vs. standing) on the magnitude of the Stroop effect. Taken together, the results suggest that posture does not influence the magnitude of the Stroop effect to the extent that was previously suggested.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".