Working memory load dissociates contingency learning and item-specific proportion-congruent effects.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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