Soft-assembled human–machine perceptual systems

Cognitive systems are highly adaptable and flexible, such that action and perception capabilities can be achieved with the body in various ways, and incorporate features of the environment and nonbiological tools. Perceptual learning refers to enduring changes to a system’s ability to perceive and respond to environmental stimuli. Here I present an integrative framework for understanding how such capabilities occur in human–machine systems comprising brain–body–tool–environment interactions. Central to this work is the claim that the capacity for high degrees of adaptation, flexibility, and learning are possible because human–machine systems are soft-assembled systems, that is, systems whose material constitution is not rigidly constrained so as to achieve goals via a variety of configurations. I begin by presenting the foundations of the framework on offer: the concepts, methods, and theories of ecological psychology; embodied cognition; dynamical systems theory; and machine intelligence. Next, I apply the framework to the case of visually-guided action. I conclude by explaining how this framework provides the explanatory and investigative tools to understand human–machine perceptual systems as soft-assembled systems that span brains-bodies-tools-environments.