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Active inference introduces a theory describing action-perception loops viathe minimisation of variational (and expected) free energy or, undersimplifying assumptions, (weighted) prediction error. Recently, activeinference has been proposed as part of a new and unifying framework in thecognitive sciences: predictive processing. Predictive processing is oftenassociated with traditional computational theories of the mind, stronglyrelying on internal representations presented in the form of generative modelsthought to explain different functions of living and cognitive systems. In thiswork, we introduce an active inference formulation of the Watt centrifugalgovernor, a system often portrayed as the canonical "anti-representational"metaphor for cognition. We identify a generative model of a steam engine forthe governor, and derive a set of equations describing "perception" and"action" processes as a form of prediction error minimisation. In doing so, wefirstly challenge the idea of generative models as explicit internalrepresentations for cognitive systems, suggesting that such models serve onlyas implicit descriptions for an observer. Secondly, we consider currentproposals of predictive processing as a theory of cognition, focusing on someof its potential shortcomings and in particular on the idea that virtually anysystem admits a description in terms of prediction error minimisation,suggesting that this theory may offer limited explanatory power for cognitivesystems. Finally, as a silver lining we emphasise the instrumental role thisframework can nonetheless play as a mathematical tool for modelling cognitivearchitectures interpreted in terms of Bayesian (active) inference.

Predictions in the eye of the beholder: an active inference account of Watt governors