Decoding the dynamics of cognitive control: Insights from reach movements and electroencephalography
Bibliographic record
Abstract
The congruency sequence effect (CSE) refers to a modulation of current-trial interference by the preceding trial in conflict tasks, such as the Eriksen flanker task, with interference typically reduced after incongruent relative to congruent trials. Although widely attributed to dynamic adjustments in cognitive control, the neural mechanisms and temporal dynamics underlying this effect remain poorly understood. Here, we combined a release-and-press flanker task with EEG decoding to examine how trial congruency and response type shape behavior and neural processing in healthy adults. Behaviorally, the CSE emerged only when responses repeated, highlighting the dominant role of stimulus-response binding over abstract control mechanisms. Neural decoding mirrored the CSE: current-trial congruency was more reliably decoded on repeated-response trials following congruent versus incongruent trials. This neural CSE was stimulus-locked, occurred between 450 and 550 ms post-stimulus onset, and was driven by theta-band activity. More broadly, congruency decoding was particularly robust over frontal channels while cross-temporal generalization indicated transient, sequential neural representations underlying control signals. Together, these findings demonstrate that both behavioral and neural signatures of the CSE are tightly constrained by response repetition and emerge within a narrow temporal window. Reach-based measures and multivariate EEG decoding jointly provide a fine-grained account of when, where, and under what conditions control-related signals unfold after conflict.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".