Working memory load dissociates contingency learning and item-specific proportion-congruent effects.
Bibliographic record
Abstract
A consistent finding in the Stroop literature is that congruency effects (i.e., the color-naming latency difference between words presented in incongruent vs. congruent colors) are larger for mostly-congruent items (e.g., the word RED presented most often in red) than for mostly-incongruent items (e.g., the word GREEN presented most often in yellow). This "item-specific proportion-congruent effect" might be produced by a conflict-adaptation process (e.g., fully focus attention to the color when the word GREEN appears) and/or by a more general learning mechanism of stimulus-response contingencies (e.g., respond "yellow" when the word GREEN appears). Under the assumption that limited-capacity resources are necessary for learning stimulus-response contingencies, we examined the contingency-learning account using both Stroop and nonconflict (i.e., noncolor words written in colors) versions of a color identification task while participants maintained a working memory (WM) load. Consistent with the contingency-learning account, WM load modulated people's ability to learn contingencies in the nonconflict task. In contrast, across 3 experiments, WM load did not affect the item-specific proportion-congruent effect in the Stroop task even though we employed a design (the "2-item set" design) in which contingency learning should be the dominant process. These results imply that the item-specific proportion-congruent effect is not merely a byproduct of contingency learning but a manifestation of reactive control, a mode of control engagement that may be especially useful when WM resources are scarce. (PsycInfo Database Record (c) 2020 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".