Pupil size correlates with heart rate, skin conductance, pulse wave amplitude, and respiration responses during emotional conflict and valence processing
Bibliographic record
Abstract
Pupil size is a non-invasive index for autonomic arousal mediated by the locus coeruleus-norepinephrine (LC-NE) system. While pupil size and its derivative (velocity) are increasingly used as indicators of arousal, limited research has investigated the relationships between pupil size and other well-known autonomic responses. Here, we simultaneously recorded pupillometry, heart rate, skin conductance, pulse wave amplitude, and respiration signals during an emotional face-word Stroop task, in which task-evoked (phasic) pupil dilation correlates with LC-NE responsivity. We hypothesized that emotional conflict and valence would affect pupil and other autonomic responses, and trial-by-trial correlations between pupil and other autonomic responses would be observed during both tonic and phasic epochs. Larger pupil dilations, higher pupil size derivative, and lower heart rates were observed in the incongruent condition compared to the congruent condition. Additionally, following incongruent trials, the congruency effect was reduced, and arousal levels indexed by previous-trial pupil dilation were correlated with subsequent reaction times. Furthermore, linear mixed models revealed that larger pupil dilations correlated with higher heart rates, higher skin conductance responses, higher respiration amplitudes, and lower pulse wave amplitudes on a trial-by-trial basis. Similar effects were seen between positive and negative valence conditions. Moreover, tonic pupil size before stimulus presentation significantly correlated with all other tonic autonomic responses, whereas tonic pupil size derivative correlated with heart rates and skin conductance responses. These results demonstrate a trial-by-trial relationship between pupil dynamics and other autonomic responses, highlighting pupil size as an effective real-time index for autonomic arousal during emotional conflict and valence processing.
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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) | 0.001 | 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".