Phase Dynamics as Bridge between Thought and Behavior
Bibliographic record
Abstract
Ongoing thoughts play a critical role in modulating cognitive performance, with phenomena such as mind wandering consistently associated with decreased task accuracy and prolonged RTs. However, the neural mechanisms underlying the influence of thought dimensions on cognition and behavior remain unclear. To elucidate this, we used EEG to investigate how two key thought dimensions-deliberate control (deliberate vs. spontaneous thoughts) and task relatedness (on-task vs. off-task thoughts)-modulate RT during a simple RT task. Behavioral results showed that both on-task and more deliberate thoughts were associated with shorter RTs compared to off-task and more spontaneous thoughts. Neurodynamic analyses revealed that on-task and deliberate thoughts were characterized by prestimulus increases in both frequency sliding, reflecting faster phase-based neural speed, and sample entropy, reflecting higher neural uncertainty/flexibility. Both prestimulus frequency sliding and sample entropy were significantly related to the degree of poststimulus intertrial phase coherence, which, in turn, correlated with RT. This sequential relationship suggests that phase-based neural dynamics play a crucial role in mediating the relationship of thought with task-related behavior. Together, these findings suggest that phase-based neural dynamics could play a key modulatory role across the divide of prestimulus and poststimulus activity in shaping the influence of ongoing thoughts (deliberate control and task relatedness) on task execution and its related behavior (RT).
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 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".