Towards a “Brain-Guided” Cognitive Architecture

Abstract

Motor control and motor cognition have been under intensive scrutiny for over a century with a growing number of experimental and theoretical tools of increasing complexity. Still we are far away from a real understanding which can allow us, for example, to integrate what we know in large-scale projects like VPH (Virtual Physiological Human). In a sense, the abundance of new behavioral, neurophysiological, and computational approaches may worsen the situation, by “flooding” researchers with frequently incompatible evidence, losing view of the overall picture. An aspect of this tendency is to quickly dismiss earlier “old-fashioned” ideas on the basis of specific but narrow new evidence. This chapter argues in the opposite direction, revisiting old-fashioned notions, like synergy formation, equilibrium point hypothesis (EPH), and body schema, in order to reuse them in a larger context, focused on whole-body actions: this context, typical of humanoid robotics, stresses the need of efficient computational architectures, capable to defeat the curse of dimensionality determined by the frightening “trinity”: complex body + complex brain + complex (partly unknown) environment. The idea is to organize the computational process in a local to global manner, grounding it on emerging studies in different areas of neuroscience, while keeping in mind that motor cognition and motor control are inseparable twins, linked through a common body/body schema. The long-term goal is to make a humanoid robot like iCub capable of “cumulative learning.” A humanoid robot should mirror both the complexity of the human form and the brain that drives it to exhibit equally complex and often creative behaviors! This requires to emulate the gradual process of infant “cognitive development” in order to investigate the underlying interplay among multiple sensory, motor, and cognitive processes in the framework of an integrated system: a coherent, purposive system that emerges from a persistent flux of fragmented, partially inconsistent episodes in which the human/humanoid perceives, acts, learns, remembers, forgets, reasons, makes mistakes, introspects, etc. We aim at linking such a model building approach with emerging trends in neuroscience, taking into account that one of the fundamental challenges today is to “causally and computationally” correlate the incredibly complex behavior of animals to the equally complex activity in their brains. This requires to build a shared computational/neural basis for “execution, imagination, and understanding” of action, while taking into account recent findings from the field of “connectomics,” which addresses the large-scale organization of the cerebral cortex, and the discovery of the “default mode network” of the brain. We will particularly focus, in the near future, on the organization of memory instead of “learning” per se because this helps understanding development from a more “holistic” viewpoint that is not restricted to “isolated tasks” or “experiments.” Computationally the proposed architecture should lead towards novel nonlinear, non-Turing computational machinery based on quasi-physical, non-digital interactions grounded in the biology of the brain.