Breathing Learning Task (BLT)

Introduced in Harrison et al. (2021), and the reason that paper matters more than a well-run anxiety study.

Predict, from a cue, whether the next breath will be hard. Find out. Rate it. Eighty times, with the rule secretly reversing four times.

Why the wiki did not have this

The wiki’s interoceptive methods divide into detection tasks (heartbeat-detection-task, filter-detection-task, respiratory-resistance-sensitivity-task, heart-rate-discrimination-task) and attention tasks (interoceptive-attention-task), plus one control task (respiratory-tracking-task). All of them ask about a static capacity: how well you read a signal, or where you point.

None of them produces the thing computational accounts of interoception are theories of. predictive-coding, interoceptive-inference and active-inference are claims about a quantity that changes on every trial — a prediction, updated by an error, weighted by a precision. A per-person accuracy score cannot test any of that. As Petzschner et al. (2021) put it, and computational-psychiatry repeats: “there is little empirical work testing such models’ predictions.”

The BLT is the instrument-shaped part of the answer. Fit a learning model to the button presses; take the model’s own per-trial estimates of prediction certainty and prediction error; use them as regressors against brain activity. The quantities the theory is about become measurements.

What comes out of it

Behaviourally, a learning rate (α ≈ 0.25 in both anxiety groups) and an inverse decision temperature. In Harrison et al. neither differed by anxiety — a null worth holding, because it means the anxiety effect in the brain occurred without any change in how fast people learned.

Computationally, two trajectories per participant, each split by valence:

• prediction certainty — how far from guessing the participant’s expectation was, separately for expectations of resistance (negative) and of no resistance (positive);
• prediction error magnitude — how wrong the expectation turned out, again split by whether the surprise was a resistance appearing or failing to appear.

Neurally, in the paper’s 7T reduced-FOV slab: certainty tracked deactivation of dlPFC, anterior insula, ACC and MFG; error magnitude tracked activation of aIns, ACC, MFG and the PAG. The signs are the ones predictive coding requires — more certainty, less updating, less activity.

The design decision worth copying

The two cues are explicitly instructed as a coupled pair: if one predicts resistance at 80%, the other predicts it at 20%, and they can only swap. Participants are told this.

That instruction is what lets the model be fitted in “contingency space” — a single trajectory rather than two, following Iglesias et al. (2013) — which roughly halves the parameters the data must support. It also means the task measures learning about one volatile rule rather than two independent ones, which is a cleaner target and a narrower one. A participant who did not believe the instruction is being modelled wrongly, and nothing checks whether they did.

What it is not

It is not a measure of interoceptive accuracy. The resistance is set at 70% of maximal inspiratory pressure; everyone feels it. Breathing-difficulty ratings did not differ between anxiety groups (82.6% vs 83.8%). The task deliberately makes perception trivial so that learning is the only thing varying — which is why the same paper runs the FDT separately for the perceptual layer.

It is not forecasting. That page defines the missing arc as prediction of how internal states evolve, including under their own intrinsic dynamics. The BLT’s learning is about an arbitrary visual cue’s relationship to an externally imposed event. The body does nothing on its own here. The gap forecasting records is narrowed only in the sense that some interoceptive prediction is now modelled and measured; the specific arc remains empty.

It is not conditioning in the pavlovian-defense-conditioning sense, though it is close: there is no measured defensive response, the outcome is a graded aversive load rather than a shock, and the dependent variable is an explicit verbal-equivalent prediction rather than a physiological CR.

Open

• The task has been run once, by its authors. No independent replication, no clinical sample, no second channel.
• The model-selection failure is unresolved. Either the models are too similar to distinguish at 80 trials, or the task does not constrain volatility learning enough to separate a fixed learning rate from an adaptive one. Longer sessions or richer readouts (continuous expectancy ratings) would settle which.
• Nothing links BLT parameters to any outcome. Learning rate is a candidate computational biomarker; here it predicted neither group membership nor any questionnaire. The biomarker claim remains unearned by this instrument.