Predicting attentional focus: Heartbeat-evoked responses and brain dynamics during interoceptive and exteroceptive processing
Bibliographic record
Abstract
Attention shapes our consciousness content and perception by increasing the probability of becoming aware and/or better encoding a selection of the incoming inner or outer sensory world. Engaging interoceptive and exteroceptive attention should elicit distinctive neural responses to visceral and external stimuli and could be useful in detecting covert command-following in unresponsive patients. We designed a task to engage healthy participants' attention toward their heartbeats or auditory stimuli and investigated whether brain dynamics and the heartbeat-evoked potential (HEP) distinguished covert interoceptive-exteroceptive attention. Exteroceptive attention yielded an overall flattening of the power spectral density (PSD), whereas during interoception, there was a decrease in complexity, an increase in frontal connectivity and theta oscillations, and a modulation of the HEP. Subject-level classifiers based on HEP features classified the attentional state of 17/20 participants. Kolmogorov complexity, permutation entropy, and weighted symbolic mutual information showed comparable accuracy in classifying covert attention and exhibited a synergic behavior with the HEP features. PSD features demonstrated exceptional performance (20/20). Command-following was assessed in five brain-injured patients with a modified version of the task. An unresponsive wakefulness syndrome/vegetative state patient and a locked-in syndrome patient demonstrated a willful modulation of the HEP and together with the explored brain markers suggest that patients were complying with task instructions. Our findings underscore the importance of attentional mechanisms in shaping interoceptive and exteroceptive sensory processing and expand the framework of heart-brain interactions employed for diagnostic purposes in patients with disorders of consciousness.
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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 | 0.000 |
| 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 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".