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

TL;DR

This paper presents an active inference model of Watt governors, challenging traditional views on internal representations and highlighting the framework's utility as a mathematical tool for cognitive modeling.

Contribution

It introduces an active inference formulation of Watt governors, questioning the role of explicit internal models and discussing predictive processing's limitations in explaining cognition.

Findings

Generative models may serve as implicit descriptions rather than explicit internal representations.

Predictive processing might have limited explanatory power for cognitive systems.

Active inference provides a useful mathematical framework for modeling cognitive architectures.

Abstract

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.