The Interplay Between Tonic and Phasic Pupil Activity and Cognitive Flexibility and Stability
Bibliographic record
Abstract
Previous research has shown that while larger phasic pupillary activity indexes lower switch costs and better performance on a Stroop task, greater tonic pupillary activity indexes greater exploration. However, the direct influence of tonic-phasic pupillary activity on cognitive flexibility and cognitive stability-two control modes that potentially trade off with each other-has not been systematically investigated. We examine these associations using a task that imposes varying requirements on flexibility (task switching) and stability (distractor inhibition). The task included ambiguous trials that captured participants' preference for cognitively flexible performance. Participants (n = 51) completed the task with pupillary measurement recording. We find a lower preference to voluntarily switch (lower flexibility preference) in individuals with higher switch costs (lower ability/effort exerted to be flexible) and in individuals with faster RTs on Distractor Inhibition trials (higher stability), indicating a possible trade off between an individual's cognitively stable performance and the preference to be flexible. Examining pupillary data, we show that a larger phasic pupillary response in Task Switch trials is associated with lower switch costs, that is, higher flexibility. Individuals with larger average tonic pupil diameter were less likely to voluntarily switch tasks in ambiguous trials (i.e., lower flexibility preference), contrary to our expectations. Finally, we observed that higher tonic pupillary measures predicted quicker errors on trials measuring cognitive stability and greater overall task disengagement. Taken together, our findings shed light on the differential relationships between phasic pupillary activity and tonic pupil diameter and stable versus flexible modes of cognitive control.
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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 | 0.000 |
| 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 | 0.002 |
| 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 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".