Target–distractor correlation does not imply causation of the Stroop effect
Bibliographic record
Abstract
In the Stroop task, the identities of the targets (e.g., colours) and distractors (e.g., words) used are often correlated. For example, in a list in which 4 words and 4 colours are combined to form 16 stimuli, each of the 4 congruent stimuli is typically repeated 3 times as often as each of the 12 incongruent stimuli. Some accounts of the Stroop effect suggest that in this type of list, often considered as a baseline because of the matching proportion of congruent and incongruent stimuli (50%), the word dimension actually receives more attention than it does in an uncorrelated list in which words and colours are randomly paired. This increased attention would be an important determinant of the Stroop effect in correlated situations, an idea supported by the observation that higher target-distractor correlation lists are associated with larger Stroop effects. However, because target-distractor correlation tends to be confounded with congruency proportion in common designs, the latter may be the crucial factor, consistent with accounts that propose that attention is adapted to the list's congruency proportion. In four experiments, we examined the idea that target-distractor correlation plays a major role in colour-word Stroop experiments by contrasting an uncorrelated list with a correlated list matched on relevant variables (e.g., congruency proportion). Both null hypothesis significance testing and Bayesian analyses suggested equivalent Stroop effects in the two lists, challenging accounts based on the idea that target-distractor correlations affect how attention is allocated in the colour-word Stroop task.
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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 | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".