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Decoding the dynamics of cognitive control: Insights from reach movements and electroencephalography

2025· preprint· en· W4413941826 on OpenAlexafffund
Moaz Shoura, K. Manon Mcnair, Adrian Nestor, Christopher D. Erb

Bibliographic record

VenueBiological Psychology · 2025
Typepreprint
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsUniversity of Toronto
FundersMarsden FundNatural Sciences and Engineering Research Council of CanadaRoyal Society Te Apārangi
KeywordsElectroencephalographyDecoding methodsDynamics (music)CognitionControl (management)Cognitive psychologyPsychologyComputer scienceCognitive scienceArtificial intelligenceNeuroscienceAlgorithm

Abstract

fetched live from OpenAlex

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 450-550 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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.594
Threshold uncertainty score0.854

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.149
GPT teacher head0.404
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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