While interoception is of major neuroscientific interest, its precise definition and delineation from exteroception continue to be debated. Here, we propose a functional distinction between interoception and exteroception based on computational concepts of sensor-effector loops. Under this view, the classification of sensory inputs as serving interoception or exteroception depends on the sensor-effector loop they feed into, for the control of either bodily (physiological and biochemical) or environmental states. We explain the utility of this perspective by examining the perception of skin temperature, one of the most challenging cases for distinguishing between interoception and exteroception. Specifically, we propose conceptualising thermoception as inference about the thermal state of the body (including the skin), which is directly coupled to thermoregulatory processes. This functional view emphasises the coupling to regulation (control) as a defining property of perception (inference) and connects the definition of interoception to contemporary computational theories of brain-body interactions.

Contemporary theories of interoception propose that the brain constructs a model of the body for predicting the states and allostatic needs of all organs, including the skin, and updates this model using prediction error signals. However, empirical tests of this proposal are scarce in humans. This computational neuroimaging study investigated the presence and location of thermoceptive predictions and prediction errors in the brain using probabilistic manipulations of skin temperature in a novel interoceptive learning paradigm. Using functional MRI in healthy volunteers, we found that a Bayesian model provided a better account of participants' skin temperature predictions than a non-Bayesian model. Further, activity in a network including the anterior insula was associated with trial-wise predictions and precision-weighted prediction errors. Our findings provide further evidence that the anterior insula plays a key role in implementing the brain's model of the body, and raise important questions about the structure of this model. ### Competing Interest Statement The authors have declared no competing interest.

Subjective expectations are known to be associated with clinical outcomes. However, expectations exist about different aspects of recovery, and few studies have focused on expectations about specific treatments. Here, we present results from a prospective observational study of patients receiving lumbar steroid injections against low back pain (N = 252). Patients completed questionnaires directly before (T1), directly after (T2), and 2 weeks after (T3) the injection. In addition to pain intensity, we assessed expectations (and certainty therein) about treatment effects, using both numerical rating scale (NRS) and the Expectation for Treatment Scale (ETS). Regression models were used to explain (within-sample) treatment outcome (pain intensity at T3) based on pain levels, expectations, and certainty at T1 and T2. Using cross-validation, we examined the models' ability to predict (out-of-sample) treatment outcome. Pain intensity significantly decreased (P < 10(-15)) 2 weeks after injections, with a reduction of the median NRS score from 6 to 3. Numerical Rating Scale measures of pain, expectation, and certainty from T1 jointly explained treatment outcome (P < 10(-15), R-2 = 0.31). Expectations at T1 explained outcome on its own (P < 10(-10),f2=0.19) and enabled out-of-sample predictions about outcome (P < 10(-4)), with a median error of 1.36 on a 0 to 10 NRS. Including measures from T2 did not significantly improve models. Using the ETS as an alternative measurement of treatment expectations (sensitivity analysis) gave consistent results. Our results demonstrate that treatment expectations play an important role for clinical outcome after lumbar injections and may represent targets for concomitant cognitive interventions. Predicting outcomes based on simple questionnaires might be useful to support treatment selection.

Psychiatry faces fundamental challenges with regard to mechanistically guided differential diagnosis, as well as prediction of clinical trajectories and treatment response of individual patients. This has motivated the genesis of two closely intertwined fields: (i) Translational Neuromodeling (TN), which develops “computational assays” for inferring patient-specific disease processes from neuroimaging, electrophysiological, and behavioral data; and (ii) Computational Psychiatry (CP), with the goal of incorporating computational assays into clinical decision making in everyday practice. In order to serve as objective and reliable tools for clinical routine, computational assays require end-to-end pipelines from raw data (input) to clinically useful information (output). While these are yet to be established in clinical practice, individual components of this general end-to-end pipeline are being developed and made openly available for community use. In this paper, we present the T ranslational A lgorithms for P sychiatry- A dvancing S cience (TAPAS) software package, an open-source collection of building blocks for computational assays in psychiatry. Collectively, the tools in TAPAS presently cover several important aspects of the desired end-to-end pipeline, including: (i) tailored experimental designs and optimization of measurement strategy prior to data acquisition, (ii) quality control during data acquisition, and (iii) artifact correction, statistical inference, and clinical application after data acquisition. Here, we review the different tools within TAPAS and illustrate how these may help provide a deeper understanding of neural and cognitive mechanisms of disease, with the ultimate goal of establishing automatized pipelines for predictions about individual patients. We hope that the openly available tools in TAPAS will contribute to the further development of TN/CP and facilitate the translation of advances in computational neuroscience into clinically relevant computational assays.