Reproducing the Location-Based Context-Specific Proportion Congruent Effect for Frequency Unbiased Items: A Reply to Hutcheon and Spieler (2016)
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Bibliographic record
Abstract
Stroop effects can be modulated by context-specific cues associated with different levels of proportion congruent, even for items that appear equally frequently in each context. This result has important theoretical implications, because it rules out frequency-driven learning explanations of context-specific proportion congruent (CSPC) effects and leaves open the possibility that a cue-driven retrieval process can reinstate attentional control settings in a rapid online fashion. The purpose of the present work was to address reproducibility concerns that have been raised about this finding. We conducted several reproductions and novel extensions using Amazon's mechanical Turk in both Stroop and flanker tasks. We successfully replicated the central finding that CSPC effects can be observed for frequency-unbiased items. We also provide new Monte Carlo simulation analyses to estimate reproducibility of the phenomena that show important limitations on these designs for measuring contextual control.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it