Modeling and controlling the body in maladaptive ways  5
Figure 1. A schematic illustration of a hierarchical active inference model. This model links (exteroceptive, interoceptive, and proprioceptive) 
sensations at lower levels with multimodal models of hidden bodily states, such as fatigue and hunger, at intermediate levels, and finally with 
temporally extended, integrative models of the embodied self at the higher hierarchical level. In this schematic, following predictive coding (Rao and 
Ballard 1999, Friston 2005), black and red circles represent neural units that encode predictions and prediction errors, respectively. The levels are 
reciprocally connected, so predictions are propagated from the top-down (black edges) and prediction errors from the bottom-up (red edges). Finally, 
the pink triangles indicate a mechanism of precision gating (or gain control) of prediction error units, which determines their relative influence on 
units encoding predictions. At a neurobiological level, prediction and prediction error units could be mapped to deep and superficial pyramidal cells in 
cortical hierarchies, whereas expected precision could be linked to neuromodulatory input. The elements of the generative model shown do not need 
to map one-to-one to specific brain areas or networks but are plausibly distributed across many of them. However, as a first approximation, the lower 
and intermediate layers of the generative model could be linked to brain networks that process unimodal information (e.g. sensory cortices for 
exteroceptive information) and multimodal association areas, respectively. The highest level of the generative model could be linked to brain networks 
that process information about the self, such as the insular cortex, the anterior cingulate cortex, and the medial prefrontal cortex. See Parr et al. 
(2022) for details about hierarchical generative models supporting adaptive regulation and allostasis and Barrett and Simmons (2015) for their 
putative neuronal underpinnings. See online article for colored version of this figure.
are reciprocally linked through top-down connections that convey 
predictions (black edges) and bottom-up connections that convey 
prediction errors (red edges), within and across levels. This predic-
tive coding architecture permits inferring (in the Bayesian sense) 
the most likely causes of sensations, across multiple modalities 
and multiple hierarchical levels, by minimizing prediction errors 
at all levels. The rationale is that predictions at all levels are con-
tinuously adjusted (and synaptic weights adjusted at a slower time 
scale) until they match with incoming multimodal stimuli suf-
ficiently well, and, consequently, the prediction errors across all 
levels are minimized. This process entails that even if a predictive 
coding agent starts with an incorrect prediction (e.g. about what 
object it is looking at) the prediction errors that measure a discrep-
ancy between the predicted sensations and the actual sensations 
can help revise the initial predictions. See Parr et al. (2022) for a 
more detailed explanation of how to interpret these schematics.
Another critical aspect of  Fig. 1 is that it illustrates two path-
ways in which prediction errors at the proprioceptive and inte-
roceptive levels are used to steer physical actions (reflex arcs) 
and autonomic actions (autonomic reflexes). Endowing predictive 
coding with these reflexes—hence realizing an “active inference” 
architecture—permits minimizing prediction errors by changing 
the state of the world (by physically acting) or the internal milieu 
(by engaging in autonomic actions) rather than only by changing 
predictions, as described later.
Equipped with a generative model like the one shown in Fig. 1, 
an active inference agent can continuously infer (and act upon) 
the state of the world and of the body, including the internal 
milieu, at multiple time scales. Of particular interest, here are