Robust evidence for proactive conflict adaptation in the proportion-congruent paradigm.
Bibliographic record
Abstract
In the standard Proportion-Congruent (PC) paradigm, performance is compared between a list containing mostly congruent (MC) stimuli (e.g., the word RED in the color red in the Stroop task; Stroop, 1935) and a list containing mostly incongruent (MI) stimuli (e.g., the word BLUE in red). The PC effect, the finding that the congruency effect (i.e., the latency difference between incongruent and congruent stimuli) is typically larger in an MC list, has been interpreted by the popular conflict-monitoring account (Botvinick et al., 2001) as reflecting a proactive process whereby attention to task-relevant information is adapted based on how frequently conflict from task-irrelevant information arises. Recently, however, alternative accounts of the PC effect have emerged that assume either that the PC effect reflects processes other than proactive conflict adaptation (e.g., stimulus-response contingency learning) or that proactive conflict adaptation is only engaged as a last resort (e.g., when contingency learning cannot be used to minimize interference). We examined these ideas in three experiments in which proactive conflict adaptation could be evaluated independently from processes that are normally confounded with it in the PC paradigm, while still allowing those processes, particularly contingency learning, to be used to minimize interference. Consistent with the conflict-monitoring account of the PC effect, but inconsistent with all the alternative accounts of the PC effect, evidence for proactive conflict adaptation emerged in all experiments. Although multiple processes may be engaged in the PC paradigm, this paradigm remains a valid tool for examining proactive conflict adaptation, its typical use. (PsycInfo Database Record (c) 2023 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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".