By self-organizing around prediction error, and by learning a generative rather than a merely discriminative (i.e., pattern-classifying) model, these approaches realize many of the goals of previous work in artificial neural networks, robotics, dynamical systems theory, and classical cognitive science. They self-organize around prediction error signals, perform unsupervised learning using a multi-level architecture, and acquire a satisfying grip—courtesy of the problem decompositions enabled by their hierarchical form—upon structural relations within a domain. They do this, moreover, in ways that are firmly grounded in the patterns of sensorimotor experience that structure learning, using continuous, non-linguaform, inner encodings (probability density functions and probabilistic inference). Precision-based restructuring of patterns of effective connectivity then allow us to nest simplicity within complexity, and to make as much (or as little) use of body and world as task and context dictate.
This is encouraging. It might even be that models in this broad ballpark offer us a first glimpse of the shape of a fundamental and unified science of the embodied mind.
Acknowledgements
This work was supported in part by the AHRC-funded ‘Extended Knowledge’ project, based at the Eidyn research centre, University of Edinburgh.