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.
Abstract Functional neuroimaging techniques, e.g. positron emission tomography (PET) and functional magnetic resonance imaging (fMRI), and neurophysiological methods, e.g. electroencephalography (EEG) and magnetoencephalograpy (MEG), are used widely in cognitive and clinical neuroscience. A common aim is to understand brain function along two dimensions: functional specialization and functional integration. Functional specialization assumes that AQ1 distinct brain regions are specialized for certain aspects of information processing, but allows for the possibility that this specialization is anatomically segregated across multiple regions. Most current functional neuroimaging experiments have adopted this view and interpret the areas that are activated by a certain task component as the elements of a distributed system. However, this characterization does not address how the locally specialized areas are bound together by context-dependent interactions among these areas, i.e. the functional integration within the system. This chapter reviews established techniques for characterizing functional integration on the basis of functional magnetic resonance imaging (fMRI) data.
It has been suggested recently that action and perception can be understood as minimising the free energy of sensory samples. This ensures that agents sample the environment to maximise the evidence for their model of the world, such that exchanges with the environment are predictable and adaptive. However, the free energy account does not invoke reward or cost-functions from reinforcement-learning and optimal control theory. We therefore ask whether reward is necessary to explain adaptive behaviour. The free energy formulation uses ideas from statistical physics to explain action in terms of minimising sensory surprise. Conversely, reinforcement-learning has its roots in behaviourism and engineering and assumes that agents optimise a policy to maximise future reward. This paper tries to connect the two formulations and concludes that optimal policies correspond to empirical priors on the trajectories of hidden environmental states, which compel agents to seek out the (valuable) states they expect to encounter.
