and current sensations in different modalities (e.g. interoceptive 
or exteroceptive). The latter (epistemic) objective is to reduce 
uncertainty about one’s estimated state. This means that active 
inference agents tend to avoid ambiguous states, encompass-
ing the avoidance of ambiguous places where self-localization is 
challenging, ambiguous social situations where safety is uncer-
tain, and ambiguous bodily states, such as unsure feelings of 
fatigue. However, one apparent exception to this aversion to ambi-
guity arises when exploring novel states implies the opportunity 
to learn new things and enhance one’s model; see Friston et al. 
(2017) for a discussion. Furthermore, and importantly, active infer-
ence agents will actively operate in the environment to reduce 
their ambiguity; for example, by actively seeking informative sen-
sations that disambiguate in which location they are (e.g. by 
looking for traffic signs), whether their social context is safe 
or unsafe (e.g. by trying to understand other’s intentions from 
their facial expressions and actions), or whether they are cur-
rently fatigued (e.g. by putting attention to one’s heart), happy,
or sad.
The last examples—disambiguating one’s fatigue and emo-
tional states—may seem strange if one assumes that we do have 
direct access to the body- and allostasis-related states (e.g. states 
of satiation, thirst, and fatigue) and to our emotions (e.g. we 
automatically know whether we are happy or sad). However, one 
assumption of active inference is that one’s bodily and emotional 
states are not necessarily observable but, instead, “hidden states” 
that need to be inferred on the basis of sensations (especially, 
but not exclusively, of interoceptive sensations from the inside 
of the body) and of an implicit, unconscious model of how the 
body functions (Barrett and Simmons 2015, Pezzulo et al. 2015, 
Seth and Friston 2016). In other words, the same inferential pro-
cess that allows active inference agents to estimate the hidden 
state of the external environment (e.g. the presence or absence of 
an object in the environment) is also used to estimate other hid-
den states, such as fatigue, happiness, or sadness. This implies 
that one can also be wrong, or be fooled, about these states; for 
example, we could experience the “interoceptive illusion” of feel-
ing more fatigued than our physiological parameters would afford 
(Iodice et al. 2019).
Extending this idea even further, one can assume that cer-
tain emotional states, as well as self-awareness and the (embod-
ied) sense of self—and the feeling of continually being the same 
person—could be constructed similarly: it would be the result of 
an inferential process that integrates bodily sensations and other 
experiences over time (Gu et al. 2013, Seth 2013, Stephan et al. 
2016, Barr
ett 2017). Figure 1 illustrates graphically this perspective 
by showing a (schematic) hierarchical generative model that links 
(exteroceptive, interoceptive, and proprioceptive) sensations at 
lower levels with multimodal models of hidden bodily states, such 
as fatigue and hunger at intermediate layers, and, finally, with 
temporally extended, integrative models of the emotional and 
embodied self at the higher hierarchical level. The hierarchical 
generative model recapitulates a simple predictive coding archi-
tecture, which includes various putative brain areas or networks 
(gray ovals) arranged hierarchically. In the schematic, networks for 
unimodal (exteroceptive, proprioceptive, and interoceptive) pro-
cessing are situated at the lowest hierarchical level, multimodal 
networks are at an intermediate level, and networks for process-
ing a persistent model of the self are at the highest level. Note 
that this simple schematic is not supposed to recapitulate brain 
anatomy but to illustrate the basic principles of hierarchical gen-
erative models and predictive coding; (for a discussion of the