6 Barca et al.
participants processed faces expressing fear (but not neutral 
faces or faces expressing other emotions) when their heart rate 
was high—hence congruent with the fearful expression (Pez-
zulo et al. 2018, Yu et al. 2021). The generative model shown 
in Fig. 1 could support this kind of inference by using interocep-
tive information from the heart (i.e.  high heart rate) as evidence 
that “there might be something fearful out there” (Pezzulo 2013). 
Another more complex example regards emotional awareness 
and self-awareness—which significantly engage the brain regions 
involved in interoception and the representation of physiologi-
cal processes (Garfinkel et al. 2013). The generative model shown 
in Fig. 1 might support processes of emotional awareness in a 
way that is neither purely bottom-up (i.e. as if interoceptive sig-
nals cause emotional awareness) nor top-down (i.e. as if emotional 
awareness causes interoceptive signals), but rather through a 
circular causality between central predictions about bodily state—
that engage autonomic reflexes—and interoceptive streams—that 
update the predictions (Seth and Friston 2016). In this perspec-
tive, any representation that induces interoceptive predictions 
could be associated with emotional or affective content; cru-
cially, this is also the case with some aspects of self-awareness 
(e.g. recognizing one’s own face) that require integrating intero-
ceptive streams with concurrent exteroceptive (e.g. visual) and 
proprioceptive cues. These examples illustrate that the genera-
tive model of  Fig. 1 natively implements both the multisensory 
integration required to unite (for example) interoceptive and exte-
roceptive streams and the active aspects that are supposed to 
support emotional and self-processing—and the construction of 
an “embodied self” (i.e. the circular causality between engag-
ing autonomic reflexes and capturing the ensuing interoceptive
signals).
In general, the accuracy of the inference of hidden bodily 
states, the “embodied self,” or other aspects of the model depends 
on the signal-to-noise ratio of the sensations and on the quality of 
the model. For example, it is difficult to self-localize in a city if it 
is dark (low signal-to-noise ratio) or if one does not know the city 
well (poor model). The inference of hidden bodily and emotional 
states might function in an analogous manner. If the quality of 
the afferent interoceptive (e.g. cardiac) signals is low, or if one has 
a poor model of how one’s body functions, then it would estimate 
one’s bodily states such as fatigue incorrectly (which in turn would 
also impair its adaptive regulation of the same bodily states). Inte-
roceptive signals could be “too noisy” for various reasons, which 
might be related to physiology, inflammation, or stress. The body 
model can be poor in various ways, too. For example, it could 
poorly characterize the statistical relations between interoceptive 
sensations and hidden bodily states (e.g. systematically mischar-
acterize high heart rate as caused by hunger but not fatigue
or joy).
Finally, there is a third essential element that determines the 
accuracy of the inference: precision control. In predictive cod-
ing, the influence of prediction errors on inference is weighted 
by their precision, i.e. inverse variance (pink triangles in  Fig. 1). 
This weighting would ensure that very reliable sensations have 
more impact on inference than unreliable sensations. However, 
precision (like all other variables) needs to be estimated, but this 
might be incorrect. An incorrect setting of precisions has been 
associated with various psychopathological conditions, such as 
psychosis (Adams et al. 2013), eating disorders (Barca and Pezzulo 
2020), panic disorders ( Maisto 
 et al. 2021), symptom perception 
(Pezzulo et al. 2019), depression (Barrett et  al. 2016), and many 
others (Khalsa et al. 2018, Paulus et al. 2019). Intuitively, assign-