From the brain’s encoding of input dynamics to its behavior: neural dynamics shape bias in decision making
Bibliographic record
Abstract
The human brain is tightly connected to the individual’s environment and its input dynamics. How the dynamics of periodic environmental stimuli influence neural activity and subsequent behavior via neural entrainment or alignment is not fully clear yet, though. This study explores how periodic environmental stimuli influence neural activity and behavior. EEG data was collected during a Go-NoGo task with a periodic intertrial interval (ITI) of 1.3 s (0.769 Hz). Results showed that the task’s temporal structure increased power spectrum activity at 0.769 Hz, which showed high intersubject variability. Higher task-periodicity effects were linked to stronger phase-based intertrial coherence (ITC) and reduced neural complexity, as measured by lower Lempel-Ziv Complexity (LZC). Additionally, higher periodicity in the power spectrum correlated with faster reaction times and stronger response bias. We conclude that the encoding of the inputs’ dynamics into the brains power spectrum shapes subsequent behavior, e.g., RT and response bias, through reducing neural complexity. This study examines how periodic stimuli affect neural activity and behavior. EEG data from a Go-NoGo task show that task periodicity influences the brain’s power spectrum, reducing neural complexity and improving reaction time and response bias.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".