Modeling brain dynamics and gaze behavior: Starting point bias and drift rate relate to frontal midline theta oscillations
Bibliographic record
Abstract
Frontal midline theta oscillatory dynamics have been implicated as an important neural signature of inhibitory control. However, most proactive cognitive control studies rely on behavioral tasks where individual differences are inferred through button presses. We applied computational modeling to further refine our understanding of theta dynamics in a cued anti-saccade task with gaze-contingent eye tracking. Using a drift diffusion model, increased frontal midline theta power during high-conflict, relative to low-conflict, trials predicted a more conservative style of responding through the starting point (bias). During both high- and low-conflict trials, increases in frontal midline theta also predicted improvements in response efficiency (drift rate). Regression analyses provided support for the importance of the starting point bias, which was associated with frontal midline theta over the course of the task above-and-beyond both drift rate and mean reaction time. Our findings provide a more thorough understanding of proactive gaze control by linking trial-by-trial increases of frontal midline theta to a shift in starting point bias facilitating a more neutral style of responding.
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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 | 0.000 | 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 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".