Much like we had to grapple with Whitehead's language, we must grapple with Friston's.
Here are three important concepts that shape his understanding on the nature of learning and biological evolution:
_Generative Model_
>The generative model is at the heart of (active) Bayesian inference. In simple terms, the generative model is just a way of formalizing beliefs about the way outcomes are caused.
>
Usually a generative model is specified in terms of the likelihood of each outcome, given their causes and the prior probability of those causes.
>
Inference then corresponds to inverting the model, which means computing the posterior probability of (unknown or hidden) causes, given observed outcomes. source 
_Active Inference_
>Active inference separates the problems of optimizing action and perception by assuming that action fulfills predictions based on inferred states of the world. Optimal predictions are therefore based on (sensory) evidence that is evaluated using a generative model of (observed) outcomes. source
_Free Energy Principle_
>The free energy principle explains how biological systems maintain their order by restricting themselves to a limited number of states which entail beliefs about hidden states in their environment. wikipedia
In this paradigm, we observe a dynamic of convergence and divergence that creates 'Bayesian belief updates' – what we hypothesize are experienced as Eureka Moments – that is at the heart of learning, creativity, and innovation, and directly relates to Schematic Accommodation theorized by Piaget.
> This leads to Bayesian belief updates that are informed by beliefs about the future (prediction) and context learning that is informed by beliefs about the past (postdiction).
We are also intrigued by the concept of Epistemic Foraging that is integral to process of active inference and provides insight into the experience of the agile mindset.
DOT FROM preview-next-diagram