Interoception and Affective Processes: Network-Based Overlap and Relationships with Psychopathology
Bibliographic record
Abstract
Interoception is increasingly recognized as an important factor in mental health, partly due to its central role in affective processes. However, it remains unclear what components are captured by self-reported interoception and whether they overlap with affective processes, like emotional awareness and regulation, hindering interpretation of relationships with psychopathology. The current study addresses this by situating the Multidimensional Assessment of Interoceptive Awareness–2 (MAIA-2) relative to established affective measures to clarify its components and relationships with psychopathology.In a community sample (N=658), network analysis investigated whether subscales from the MAIA-2, the Toronto Alexithymia Scale – 20, and the Emotion Regulation Questionnaire form cross-measure communities, reflecting divergent components. Structural equation modeling (SEM) further assessed coherence of the communities and associations with dimensional psychopathology (Personality Inventory for DSM-5-Brief Form).Network analyses identified a robust three-community structure: Primary Interoception (noticing body signals), Secondary Interoception (responses to body signals), and Affective Distance (poor awareness and acceptance of affect). SEM supported these as coherent components with unique patterns of relationships with dimensional psychopathology. Secondary Interoception and Affective Distance components were particularly associated with psychopathology. Notably, MAIA-2 subscales participated in all three communities and replicated community-level psychopathology relationships.By clarifying the complex structure of the interoceptive–affective construct space, particularly in relation to the MAIA-2, this work improves interpretation of self-report findings and highlights where interoception aligns with or diverges from affective processes. Furthermore, it provides a foundation for more precise hypotheses, clearer characterization of interoceptive components relevant to psychopathology, and ultimately, better identification of intervention targets.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".