Automatic and deliberate control of action: an embodied perspective of artificial and biological brains
Giovanni Maffei · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · 2018
Animals evolved to survive in dynamic environments by developing multiple behavioral strategies to adapt and to learn from their interaction with the world. Associative mechanisms and internal representations are at the core brain computation, however, to acquire a complete knowledge of their relevance for behavior it is necessary to take into consideration their embodied nature. In an interdisciplinary effort which integrates methods from computational modeling, robotics, and electrophysiology, this dissertation presents a series of studies that aim at advancing the understanding of the automatic and deliberate processes that regulate embodied control of action in the brain. Through the formulation of a biologically constrained control architecture engaged in a real-world foraging task, we lay the ground for modeling and analyzing complex goal-oriented behavior emerging from the interplay between the automatic cerebro-cerebellar system acquiring sensory-motor associations, and the deliberate fronto-hippocampal system providing goal-oriented navigation and planning. Following the behavioral analysis of the stimulus-response model of cerebellar learning, we later ask how could the cerebellum implement anticipatory control, which is both adaptive and resistant to uncertainty. To answer this question, we explore the properties of the automatic control system and advance a novel hypothesis on the role of the cerebellum, by recasting its computation in the perceptual domain. Finally, we ask how the automatic and deliberate systems interact during unexpected situations that require a sudden change of plans. By analyzing the neural dynamics of the human frontal cortex in the control of deliberate action switch, we support the contribution of low-frequency oscillatory dynamics within this area to orchestrate behavior, based on internal representations of goals and rules. Altogether these results contribute to our understanding of how automatic and deliberate processes control action in the brain and advance novel insights that challenge or extend current theories. Despite the main aim to understand the brain, these insights could also be applied to the development of novel control systems for a new generation of robots.