Sensorimotor control as the result of the synergistic coupling of feedback and feedforward control through counterfactual errors
Abstract:
Motor control is usually seen as the result of a gradual replacement of feedback by feedforward control. The perceptual states that inform this process are considered to be defined through qualitatively different processes giving rise to the classical distinction between perceptual and behavioral learning. We have addressed this question from the perspective of an integrated architecture called the Distributed Adaptive Control (DAC) theory of mind and brain. DAC proposes that the brain is a multi-layer control system which optimizes the how of action by considering why (motivation), what (objects), where (space), when (time) and who (agents) or the H5W problem. We have shown that for DAC to realize optimal solutions in foraging problems its decision making renders policies that simultaneously optimize perceptual evidence, memory bias, goals and utility. This raises the question what the principles are that underlie the processing and adaptation of these factors. In this presentation I will focus on a link between policy adaptation and perceptual learning we have recently advanced. The dominant model of anticipatory motor control relies on the notion of an inverse model that by learning from encountered errors acquires corrective responses that supersede feedback control. However, this model by necessity faces problems in handling exceptions, such as observed in probe trials, where fast feedback control is required. We solve this challenge by proposing that adaptive motor control can also be obtained by relying on a cascade of purely sensory predictions that feed feedback control via counterfactual errors or Hierarchical Sensory Predictive Control. Using robot experiments we have demonstrated the robustness of this solution and we have found further supporting evidence in experiments with stroke patients, intracranial physiology and the detailed anatomy and physiology of the cerebellum and its dense interaction with the forebrain.
Reference:
Verschure, P. F., & Althaus, P. (2003). A real‐world rational agent: unifying old and new AI. Cognitive science, 27(4), 561-590.
Maffei, G., Herreros, I., Sanchez-Fibla, M., Friston, K. J., & Verschure, P. F. (2017). The perceptual shaping of anticipatory actions. Proc. R. Soc. B, 284(1869), 20171780.