8 Barca et al.
prior is very high, the posterior will closely reflect the prior, ren-
dering the inference rigid and incapable of adapting to changing 
environmental conditions—which might be especially problem-
atic in periods of significant changes, such as adolescence or more 
simply when one changes city, working environment, and friends. 
Furthermore, as shown in Fig. 1, hierarchical predictive coding 
architectures have precision values associated with every hierar-
chical level (whereas, for simplicity, the inference shown in Fig. 2 
is not hierarchical). The correct balance of precision parameters 
within and across layers is crucial for accurate inference, as it 
ensures that the correct levels of confidence are assigned to data 
and prior information.
Finally, and importantly, aberrant precision control (as well 
as various combinations of other factors discussed earlier, such 
as noisy bodily sensations and poor bodily mode) can render 
inference not just incorrect but also highly ambiguous, leaving a 
person in a permanent condition of uncertainty about whether 
one is fatigued (when considering the bodily state), happy, or sad 
(when considering the emotional state), what kind of person one 
is or what are one’s desires (when considering self-models), etc. 
Importantly, this condition of uncertainty is not limited to percep-
tual inference but has a cascade effect on decision-making and 
action selection. Indeed, an uncertain estimate of one’s state auto-
matically implies that one has low confidence in the effects of 
one’s plans; for example, it renders more difficult the prediction 
of whether a run would be too fatiguing or a party too stressful. 
It is exactly this kind of uncertainty (about the present and the 
future, the body state or the outcomes of social interactions, etc.) 
that active inference agents strive to avoid.
Avoiding excessive uncertainty in maladaptive 
ways
Our previous discussion clarified that active inference agents have 
sophisticated (hierarchically deep, temporally extended) models 
of themselves that permit making inferences at multiple levels 
about hidden bodily states (which comprise both the classical 
“body schema” and other states that are relevant for allostasis, 
such as hunger, thirst, and fatigue) and other states related to 
the emotional and embodied self. These models are essential for 
ensuring effective regulation and control at multiple levels, from 
simple reflexes to sophisticated goal-directed behaviors (Tschantz 
et al. 2022). However, in some cases, the aforementioned inferen-
tial process might not work properly (e.g. if the sensory channels 
are too noisy or are assigned excessively high or low precision). 
As a consequence, a person could experience an excessive or 
irreducible uncertainty about her bodily and emotional states or 
about the self, which in turn translates into a loss of confidence 
about which future courses of action could produce desired out-
comes. Crucially, active inference agents follow the imperative 
to avoid such an uncertainty about the present or the future. 
Normally, uncertainty minimization strategies are adaptive (e.g. 
seeking advice if one is uncertain about the direction of the pre-
ferred restaurant). However, in some conditions, such as when 
a person experiences excessive and irreducible uncertainty and 
when the uncertainty is particularly distressing or related to fun-
damental life concerns, she might potentially seek “maladaptive” 
ways to reduce it—or methods that reduce uncertainty at the cost 
of hindering fundamental imperatives of well-being and survival 
(see also Linson et al. 2020).
In this perspective, apparently paradoxical actions, such as 
food restriction and self-injurious behaviors, might be pursued 
because they could contribute to reducing the (otherwise unman-
ageable) uncertainty about bodily and emotional states or the self. 
In other words, in some conditions, the self-injuring pain could be