Dynamics of brainstem arousal systems and pupil size predict cortical interactions for flexible decision-making
Bibliographic record
Abstract
Abstract Most perceptual decisions entail a flexible mapping from sensory input to motor output. Flexible input-output mapping is reflected in correlated variability within the cortical network involved in perceptual decisions. Here, we tested the idea that brainstem arousal systems are involved in flexible input-output mapping and changes in cortical correlated variability. We combined brainstem fMRI, pupillometry, and time-resolved assessment of the intrinsic correlations between cortical population codes for stimulus and action. Human participants reported the orientation of visual stimuli by button presses, while the required stimulus-response mapping rule could undergo hidden and unpredictable changes. Rule switches evoked brainstem and pupil responses as well as changes in computational model-inferred, latent variables. These variables governed participants’ rule-switching behavior and pupil responses. Brainstem activity and pupil dilation preceded increases in the strength of correlations between cortical stimulus and action codes. Brainstem arousal systems may promote the reorganization of sensorimotor cortical pathways for flexible decisions.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| 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".