A Machine Learning Approach Towards the Differentiation Between Interoceptive and Exteroceptive Attention
Bibliographic record
Abstract
Abstract Interoception, the representation of the body’s internal state, plays a central role in emotion, motivation, and wellbeing. Interoceptive attention is qualitatively different from attention to the external senses and may recruit a distinct neural system, but the neural separability of interoceptive and exteroceptive attention is unclear. We used a machine learning approach to classify neural correlates of interoceptive and exteroceptive attention in a randomized control trial of interoceptive training (MABT). Participants in the training and control groups attended fMRI assessment before and after an 8-week intervention period (N = 44 scans). The imaging paradigm manipulated attention targets (breath vs. visual stimulus) and reporting demands (active reporting vs. passive monitoring). Machine learning models achieved high accuracy in distinguishing between interoceptive and exteroceptive attention using both in-sample and more stringent out-of-sample tests. We then explored the potential of these classifiers in “reading out” mental states in a sustained interoceptive attention task. Participants were classified as maintaining an active reporting state for only ∼90s of each 3-minute sustained attention period. Within this active period, interoceptive training enhanced participants’ ability to sustain interoceptive attention. These findings demonstrate that interoceptive and exteroceptive attention engage reliable and distinct neural networks; machine learning classifiers trained on this distinction show promise for assessing the stability of interoceptive attention, with implications for the future assessment of mental health and treatment response.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".