Modeling and controlling the body in maladaptive ways  7
Figure 2. A simplified example of (Bayesian) inference of one’s heart rate. First panel: simulated time series of heartbeat observations. Second panel: 
Shannon surprise of a generative model composed of a fixed prior about heart rate (a Gaussian with a mean of 67 and a precision of 0.11) and a 
likelihood (a Gaussian centered on the current heart rate with an additional bias of 15 pulses, with various precisions that vary between 0.47 and 10, 
see the legend). Third panel: Bayesian surprise, which measures the discrepancy between posterior and prior probabilities over time. Bottom panels: 
the two series of panels are organized in two (left and right) columns, which show the first five time steps of inference for the two cases with high 
precision (of 10) and low precision (of 0.1) of the likelihood, respectively. See the main text for an explanation and online article for colored version of 
this figure.
the current model generate significant surprise, and sometimes, 
the surprise can remain relatively high for long periods before the 
model adapts (or the world changes), especially with some param-
eterizations of the generative model. This is particularly relevant 
in this context since active inference agents strive to minimize 
their surprise (and the long-term average of surprise, entropy, 
which is a measure of uncertainty) by changing their model, or 
changing the world, or both.
Second, these examples illustrate the importance of precision 
control and the appropriate setting of precision parameters in 
guiding inference. Remarkably, the inference can be more or less 
accurate or fast using the same data, depending on the precision 
parameters. Note that in Fig. 2, we manipulated only the precision 
of the likelihood. However, it would also be possible to manipulate 
the precision of the prior, together or in alternative to the precision 
of the likelihood. Generally speaking, when the precision of the