Brain–Pupil Coupling Revealed Through Deep Learning of Intracranial Recordings
Bibliographic record
Abstract
Pupillary responses are windows into human cognition, but their neural substrates are poorly understood. We studied brain-pupil coupling through intracranial recordings and pupillometry in 13 children and youth with epilepsy (ages 9-18) during an attentional set-shifting task. Time-resolved mixed-effects modelling identified associations between pupil diameter, neural activity and cognitive performance. We first showed that pupillary dynamics are closely linked to cognitive performance, with task-stage dependencies. Larger pupil sizes prior to stimulus onset were associated with faster reaction times, whereas smaller pupil sizes during and after stimulus presentation were linked to better performance. Next, linear models identified associations between band-limited power in task-relevant neural networks and pupil size changes during the task. Finally, deep learning models based on intracranial neural activity captured patterns predictive of changes in pupil size in five of seven participants that generalised to recordings from a separate day. Using salience-based gradient mapping, we identified a network of task-relevant cortical and subcortical regions whose engagement was consistently associated with higher model performance in predicting pupil dynamics during attentional set-shifting. Our findings suggest pupillary responses are coordinated with goal-oriented cognitive processing, providing a basis for modelling cognitive functions through pupillary dynamics.
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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 | 0.001 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".