Proactive control in the Stroop task: A conflict-frequency manipulation free of item-specific, contingency-learning, and color-word correlation confounds.
Bibliographic record
Abstract
In the Stroop task, congruency effects (i.e., the color-naming latency difference between incongruent stimuli, e.g., the word BLUE written in the color red, and congruent stimuli, e.g., RED in red) are smaller in a list in which incongruent trials are frequent than in a list in which incongruent trials are infrequent. The traditional explanation for this pattern is that a conflict-monitoring mechanism adjusts attention to task-relevant versus task-irrelevant information in a proactive fashion based on list-wide conflict frequency. More recently, however, multiple alternative explanations have been advanced that could explain the pattern without invoking this form of proactive control: Individuals might only adapt to conflict frequency specific to individual items (as opposed to list-wide conflict frequency), they could learn word-color contingencies (e.g., how often a particular word and color are paired), or they could adapt attention based on whether the words are informative of the color (even if many word-color pairings are incongruent) in the list as a whole. To examine this issue, we designed a new paradigm that should eliminate any impact of these alternative mechanisms. In that paradigm, the proportion of neutral (e.g., XXX in red) and incongruent stimuli was manipulated across lists. Paralleling the results in the original paradigm, there was a smaller latency difference between incongruent and neutral stimuli in a list in which incongruent trials were frequent than in a list in which incongruent trials were infrequent, suggesting that proactive control in response to list-wide conflict frequency is a process humans can and do use. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".