Active inference introduces a theory describing action-perception loops via the minimisation of variational free energy or, under simplifying assumptions, (weighted) prediction error. Recently, active inference has been proposed as part of a new and unifying framework in the cognitive sciences: predictive processing. Predictive processing is often associated with traditional computational theories of the mind, strongly relying on internal representations presented in the form of generative models thought to explain different functions of living and cognitive systems. In this work, we introduce an active inference formulation of the Watt centrifugal governor, a system often portrayed as the canonical “anti-representational” metaphor for cognition. We identify a generative model of a steam engine for the governor, and derive a set of equations describing “perception” and “action” processes as a form of prediction error minimisation. In doing so, we firstly challenge the idea of generative models as explicit internal representations for cognitive systems, suggesting that such models serve only as implicit descriptions for an observer. Secondly, we consider current proposals of predictive processing as a theory of cognition, focusing on some of its potential shortcomings and in particular on the idea that virtually any system admits a description in terms of prediction error minimisation, suggesting that this theory may offer limited explanatory power for cognitive systems. Finally, as a silver lining we emphasise the instrumental role this framework can nonetheless play as a mathematical tool for modelling cognitive architectures interpreted in terms of Bayesian (active) inference.

The free energy principle describes cognitive functions such as perception, action, learning and attention in terms of surprisal minimisation. Under simplifying assumptions, agents are depicted as systems minimising a weighted sum of prediction errors encoding the mismatch between incoming sensations and an agent’s predictions about such sensations. The “dark room” is defined as a state that an agent would occupy should it only look to minimise this sum of prediction errors. This (paradoxical) state emerges as the contrast between the attempts to describe the richness of human and animal behaviour in terms of surprisal minimisation and the trivial solution of a dark room, where the complete lack of sensory stimuli would provide the easiest way to minimise prediction errors, i.e., to be in a perfectly predictable state of darkness with no incoming stimuli. Using a process theory derived from the free energy principle, active inference, we investigate with an agent-based model the meaning of the dark room problem and discuss some of its implications for natural and artificial systems. In this set up, we propose that the presence of this paradox is primarily due to the long-standing belief that agents should encode accurate world models, typical of traditional (computational) theories of cognition.

The assumption that action and perception can be investigated independently is entrenched in theories, models and experimental approaches across the brain and mind sciences. In cognitive science, this has been a central point of contention between computationalist and 4Es (enactive, embodied, extended and embedded) theories of cognition, with the former embracing the “classical sandwich”, modular, architecture of the mind and the latter actively denying this separation can be made. In this work we suggest that the modular independence of action and perception strongly resonates with the separation principle of control theory and furthermore that this principle provides formal criteria within which to evaluate the implications of the modularity of action and perception. We will also see that real-time feedback with the environment, often considered necessary for the definition of 4Es ideas, is not however a sufficient condition to avoid the “classical sandwich”. Finally, we argue that an emerging framework in the cognitive and brain sciences, active inference, extends ideas derived from control theory to the study of biological systems while disposing of the separation principle, describing non-modular models of behaviour strongly aligned with 4Es theories of cognition.

Active inference is emerging as a possible unifying theory of perception and action in cognitive and computational neuroscience. On this theory, perception is a process of inferring the causes of sensory data by minimising the error between actual sensations and those predicted by an inner generative (probabilistic) model. Action on the other hand is drawn as a process that modifies the world such that the consequent sensory input meets expectations encoded in the same internal model. These two processes, inferring properties of the world and inferring actions needed to meet expectations, close the sensory/motor loop and suggest a deep symmetry between action and perception. In this work we present a simple agent-based model inspired by this new theory that offers insights on some of its central ideas. Previous implementations of active inference have typically examined a “perceptionoriented” view of this theory, assuming that agents are endowed with a detailed generative model of their surrounding environment. In contrast, we present an “action-oriented” solution showing how adaptive behaviour can emerge even when agents operate with a simple model which bears little resemblance to their environment. We examine how various parameters of this formulation allow phototaxis and present an example of a different, “pathological” behaviour.