Interoception vs. Exteroception: Cardiac interoception competes with tactile perception, yet also facilitates self-relevance encoding
Bibliographic record
Abstract
Internal bodily signals, notably the heartbeat, influence our perception of the external world-but the nature of this influence remains unclear. Different frameworks, originating in opposing views of the function of interoception, have developed largely in parallel. One line of evidence (Internal/External Competition) indicates that interoceptive and exteroceptive inputs compete for neural resources. Another line (Self-related Facilitation) shows a link between interoceptive and self-related processing, which might include computing the self-relevance of exteroceptive inputs. We contrasted these accounts within a single experimental task for which they yielded distinct predictions. We measured heartbeat-evoked potentials (HEPs, a measure of cardiac interoception) with electroencephalogram and manipulated the self-relevance of an audio-tactile stimulus by placing the audio source either inside or outside the peripersonal space immediately around the body. On the one hand, prestimulus HEP amplitudes over the somatosensory cortex were linked to slower reaction times and affected audio-tactile stimulus-evoked responses in the same area, indicating competition for shared neural resources. On the other hand, prestimulus HEPs over integrative sensorimotor and default-mode network regions facilitated stimulus self-relevance encoding, both in reaction times and audio-tactile evoked responses. Importantly, Competition and Facilitation effects were spatially and statistically independent from each other. We therefore reconcile the two views by showing the coexistence of two independent mechanisms: one that allocates neural resources to either internal bodily signals or the external world, and another by which interoception and exteroception are combined to determine the self-relevance of external signals. Our results highlight the multidimensionality of HEPs and of internal states more generally.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".