A spatial version of the Stroop task for examining proactive and reactive control independently from non-conflict processes
Bibliographic record
Abstract
Conflict-induced control refers to humans' ability to regulate attention in the processing of target information (e.g., the color of a word in the color-word Stroop task) based on experience with conflict created by distracting information (e.g., an incongruent color word), and to do so either in a proactive (preparatory) or a reactive (stimulus-driven) fashion. Interest in conflict-induced control has grown recently, as has the awareness that effects attributed to those processes might be affected by conflict-unrelated processes (e.g., the learning of stimulus-response associations). This awareness has resulted in the recommendation to move away from traditional interference paradigms with small stimulus/response sets and towards paradigms with larger sets (at least four targets, distractors, and responses), paradigms that allow better control of non-conflict processes. Using larger sets, however, is not always feasible. Doing so in the Stroop task, for example, would require either multiple arbitrary responses that are difficult for participants to learn (e.g., manual responses to colors) or non-arbitrary responses that can be difficult for researchers to collect (e.g., vocal responses in online experiments). Here, we present a spatial version of the Stroop task that solves many of those problems. In this task, participants respond to one of six directions indicated by an arrow, each requiring a specific, non-arbitrary manual response, while ignoring the location where the arrow is displayed. We illustrate the usefulness of this task by showing the results of two experiments in which evidence for proactive and reactive control was obtained while controlling for the impact of non-conflict processes.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".