  Active inference introduces a theory describing action-perception loops via
the minimisation of variational (and expected) 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.
