  Recent advances in theoretical biology suggest that basal cognition and
sentient behaviour are emergent properties of in vitro cell cultures and
neuronal networks, respectively. Such neuronal networks spontaneously learn
structured behaviours in the absence of reward or reinforcement. In this paper,
we characterise this kind of self-organisation through the lens of the free
energy principle, i.e., as self-evidencing. We do this by first discussing the
definitions of reactive and sentient behaviour in the setting of active
inference, which describes the behaviour of agents that model the consequences
of their actions. We then introduce a formal account of intentional behaviour,
that describes agents as driven by a preferred endpoint or goal in latent
state-spaces. We then investigate these forms of (reactive, sentient, and
intentional) behaviour using simulations. First, we simulate the aforementioned
in vitro experiments, in which neuronal cultures spontaneously learn to play
Pong, by implementing nested, free energy minimising processes. The simulations
are then used to deconstruct the ensuing predictive behaviour, leading to the
distinction between merely reactive, sentient, and intentional behaviour, with
the latter formalised in terms of inductive planning. This distinction is
further studied using simple machine learning benchmarks (navigation in a grid
world and the Tower of Hanoi problem), that show how quickly and efficiently
adaptive behaviour emerges under an inductive form of active inference.

Neurons and Cognition;Machine Learning