Harrison et al. (2021) — Interoception of breathing and its relationship with anxiety
Neuron 109(24):4080–4093, open access (CC BY). From the Translational Neuromodeling Unit — Klaas Stephan’s group at UZH/ETH Zurich, with Petzschner on the author list — which is the same address that produced the computational review the wiki reads as the map of this quadrant.
The wiki has been waiting for this paper by name. Allen (2026)‘s page listed it as “the Filter Detection Task source. Still unprocessed”; the RRST page put it in a three-row table of respiratory instruments as the only one without a page of its own. It arrives carrying rather more than a task.
Why this is the ingest the computational thread needed
Petzschner’s page records her own verdict on the framework she works inside: “there is little empirical work testing such models’ predictions,” and the few tests she can cite include her own. computational-psychiatry repeats it. forecasting is filed as a gap page on the same grounds.
This is a test. Not of forecasting in the strict sense (nothing here models how a bodily state evolves under its own dynamics — the contingency between a cue and a resistance is what is learned), but of the claim one level down: that interoceptive predictions and prediction errors are quantities the brain computes trial by trial, and that a fitted learning model can find them. Sixty participants, eighty trials each, a generative model per person, and brain activity regressed on the model’s own per-trial estimates.
The result is affirmative and modest in the right places. Prediction certainty and prediction error magnitude both have neural correlates, they sit where the theory says (aIns, ACC, PAG), and the signs are the ones predictive coding requires — activity falls as predictions get more certain and rises with error. Before this, the wiki’s evidence for interoceptive predictive coding was cytoarchitecture (agranular visceromotor cortex), theory, and one mouse study (Livneh et al. via Berntson & Khalsa). Now there is a human, model-based, trial-resolved demonstration. See feedforward-vs-predictive-interoception.
The design, in three layers
The paper’s structure is an argument: interoception spans levels, so measure several at once and ask which level anxiety attaches to.
| level | instrument | what anxiety did |
|---|---|---|
| subjective belief | 12 questionnaires (ASI-3, MAIA, BPQ, PCS-B, PVQ-B, …) | large, highly significant differences on nearly all |
| perceptual sensitivity + metacognition | filter-detection-task | worse sensitivity, lower confidence, equal insight |
| trial-by-trial learning + brain | breathing-learning-task at 7T | no behavioural difference; a valence x group interaction in aIns |
Groups were sixty healthy adults pre-screened on the Spielberger trait scale — thirty scoring 20–25, thirty scoring ≥35 — matched for age and sex (15 women per group). Eight further participants supplied the model priors; fifteen more, not selected on anxiety, were held out to validate the fitted trajectory (β = 3.1, t = 12.1).
The finding the wiki should carry forward: it is the high levels that track anxiety
The PCA is the paper’s own summary of itself, and it inverts the direction the field’s measurement effort points.
PC1 separated the two anxiety groups at p < 1×10⁻¹¹. Its loadings, in order: depression, state anxiety, anxiety sensitivity, anxiety-disorder symptoms — then breathing catastrophizing and negative interoceptive awareness — then negative metacognitive bias, body perception, negative metacognitive performance — then perceptual threshold and decision bias — and last, aIns activity.
Read down that list. The relationship between anxiety and breathing interoception is carried overwhelmingly by what people believe and say about their bodies, secondarily by their confidence, and least by how well they actually detect a resistive load or by what their insula does. The authors are careful about the obvious rejoinder — higher levels may simply be measured less noisily than psychophysical thresholds, so the ordering may be detectability rather than strength — and the wiki should keep that caveat attached.
But taken at face value it is a direct empirical statement of something interoceptive-taxonomy has only been able to assert structurally: sensibility and accuracy are not two measurements of one thing, and the clinically interesting variance is in the first. It converges with Banellis et al.’s finding that the construct which travels between channels is confidence rather than competence — reached here from within a single channel, by asking which level of one hierarchy an affective trait attaches to.
Anxiety and respiratory sensitivity run opposite to anxiety and cardiac sensitivity
This is the most consequential collision the ingest produces, and it is not a contradiction.
The wiki’s panic literature is emphatic in one direction: panic patients detect their heartbeats better than controls (Ehlers 1993; replicated in analogue panickers by Zoellner & Craske 1999), and better cardiac perception predicts worse clinical outcome. “Anxious people are better at reading their bodies” is the premise the cognitive-model-of-panic and much of is-more-interoceptive-awareness-better run on.
Harrison et al. find the reverse in the lungs: moderate-anxiety participants needed more filters to reach threshold — they were less sensitive to an inspiratory load. And this is not an outlier; the authors cite it as replicating prior respiratory findings (Garfinkel et al. 2016a; Tiller, Pain & Biddle 1987).
Two readings, and the wiki should hold both:
- Channel-specific, and evidence for organ-specificity. Anxiety relates to the heart and to the lungs in opposite directions, which is a stronger form of decorrelation than a null correlation — not merely “these axes do not covary” but “the same trait predicts them with opposite sign.” Filed on is-interoception-domain-general.
- Instrument-specific, and evidence against the cardiac task. The counting task has a known failure mode in which anxiety manufactures accuracy (count faster in a population that undercounts; see anxiety-sensitivity and Van der Does’s artefact argument). The FDT is a two-alternative decision about a physically specified stimulus with no such route. On this reading the cardiac direction is the artefact and the respiratory direction is the finding.
Nothing here decides between them, and the discriminating study — both tasks, one anxious sample — has not been run. But reading 2 is the more parsimonious, and it makes the respiratory result the cleaner of the two.
What anxiety did to the brain, and what it did not
The one significant group effect in the imaging is an interaction between prediction valence and anxiety group in bilateral anterior insula, on prediction certainty. Low-anxiety participants showed greater aIns deactivation scaling with predictions of upcoming resistance than with predictions of no resistance; moderate-anxiety participants showed the opposite. So the shift is in how confidently-held predictions about threatening bodily states are represented — in line with Paulus & Stein’s insular account of anxiety, which the paper cites approvingly.
What is absent matters as much. No group difference and no interaction anywhere in prediction error — which several theories in this wiki predicted. Barrett & Simmons’s EPIC illness hypothesis, as computational-psychiatry records it, locates psychopathology in elevated interoceptive prediction error decoupled from priors. Brewer, Murphy & Bird’s atypical-interoception review and Paulus & Stein (2006, 2010) also point at the error side. Harrison et al. name the disagreement explicitly: their result “contrast[s] with some previously proposed hypotheses.”
That is a genuine empirical constraint on the wiki’s most-used clinical story, and it is the first one the wiki holds. It is not a refutation — anxiety is not depression, a resistive load is not dyshomeostasis, and a null in a reduced field of view at n=58 is a weak null. But every page here that explains a disorder by “elevated prediction error” now has one direct test to answer to, and the test found the effect on the other term.
The insula does both, and the PAG does one
Independent of anxiety, the whole-slab results are the clean part of the paper.
- Prediction certainty → deactivation of dlPFC, anterior insula, ACC and MFG. Certainty reduces belief updating, so activity falls as certainty rises. This is the sign predictive coding requires (Feldman & Friston 2010).
- Prediction error magnitude → activation of aIns, ACC, MFG and the periaqueductal gray.
- Valence mattered only for errors: unexpectedly receiving a resistance activated left posterior insula more than unexpectedly escaping one.
Two things worth pinning down for insular-cortex. First, the anterior insula deactivates for certainty and activates for error — one region holding both sides of the update equation, which is what a region “representing and updating models of the body” should look like, and which the wiki’s usual posterior-error/anterior-prediction split does not straightforwardly predict. Second, and the authors say so, the anterior/posterior dissociation of predictions from prediction errors that Seth, Barrett & Simmons and Stephan et al. all propose did not appear. The single crumb for it is that valence effect in posterior insula, which the authors read as homeostatically-relevant inputs being enhanced when they threaten homeostasis.
The PAG result is the other novelty: it tracked prediction error magnitude but not prediction certainty, which fits its assignment as a homeostatic-control structure receiving regulatory error rather than holding beliefs. See periaqueductal-gray.
The methodological standard, which is the highest in this wiki
Worth recording because most of what the wiki ingests does not do this: a time-stamped pre-registered analysis plan on GitLab (including the fallback rule that produced the Rescorla-Wagner choice); simulated parameter recovery and model identifiability before fitting; a held-out validation sample; a null-model likelihood-ratio check per participant, with the two failures excluded; an independent code review; summary statistical maps deposited publicly and code archived on Zenodo. Raw data are withheld for GDPR reasons with a stated request route.
The pre-registration did real work rather than decorative work: no model reached the pre-specified 90% PXP threshold, and the plan’s fallback — take the simplest — is what licensed using RW rather than a post-hoc justification. That is also the paper’s largest weakness, and the two facts are the same fact.
Placement
Read as the wiki’s first empirical, model-based test of interoceptive predictive coding in humans, and as its most complete single-channel measurement of the interoceptive hierarchy in one sample. It contradicts nothing here. What it does is supply three things the wiki has been asking for by name: an anxiety measure against a non-cardiac interoceptive task (anxiety-sensitivity’s standing gap), a first-hand source for the FDT, and evidence about where in the hierarchy an affective trait attaches — which turns out to be the level furthest from the body.