Nonmodular architectures of cognitive systems based on active inference

Cognitive systems are often modeled as input/output devices with separate perceptual and motor modules, a view that resonates with the separation principle of control theory. This paper presents a minimal sensorimotor model based on that principle and shows its limitations when external forces—such as environmental perturbations or interference from other agents—are not accounted for. As an alternative, the authors propose a nonmodular architecture grounded in active inference, which demonstrates robustness to unknown external inputs. In linear models, this robustness is achieved through a mechanism equivalent to integral control, offering a principled way to handle disturbances that the agent cannot directly control.

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Abstract

In psychology and neuroscience it is common to describe cognitive systems as input/output devices where perceptual and motor functions are implemented in a purely feedforward, open-loop fashion. On this view, perception and action are often seen as encapsulated modules with limited interaction between them. While embodied and enactive approaches to cognitive science have challenged the idealisation of the brain as an input/output device, we argue that even the more recent attempts to model systems using closed-loop architectures still heavily rely on a strong separation between motor and perceptual functions. Previously, we have suggested that the mainstream notion of modularity strongly resonates with the separation principle of control theory. In this work we present a minimal model of a sensorimotor loop implementing an architecture based on the separation principle. We link this to popular formulations of perception and action in the cognitive sciences, and show its limitations when, for instance, external forces are not modelled by an agent. These forces can be seen as variables that an agent cannot directly control, i.e., a perturbation from the environment or an interference caused by other agents. As an alternative approach inspired by embodied cognitive science, we then propose a nonmodular architecture based on the active inference framework. We demonstrate the robustness of this architecture to unknown external inputs and show that the mechanism with which this is achieved in linear models is equivalent to integral control.