Author:
Hipólito Inês,Baltieri Manuel,Friston Karl,Ramstead Maxwell J. D.
Abstract
AbstractWhen someone masters a skill, their performance looks to us like second nature: it looks as if their actions are smoothly performed without explicit, knowledge-driven, online monitoring of their performance. Contemporary computational models in motor control theory, however, are instructionist: that is, they cast skillful performance as a knowledge-driven process. Optimal motor control theory (OMCT), as representative par excellence of such approaches, casts skillful performance as an instruction, instantiated in the brain, that needs to be executed—a motor command. This paper aims to show the limitations of such instructionist approaches to skillful performance. We specifically address the question of whether the assumption of control-theoretic models is warranted. The first section of this paper examines the instructionist assumption, according to which skillful performance consists of the execution of theoretical instructions harnessed in motor representations. The second and third sections characterize the implementation of motor representations as motor commands, with a special focus on formulations from OMCT. The final sections of this paper examine predictive coding and active inference—behavioral modeling frameworks that descend, but are distinct, from OMCT—and argue that the instructionist, control-theoretic assumptions are ill-motivated in light of new developments in active inference.
Funder
Social Sciences and Humanities Research Council of Canada
University of Wollongong
Wellcome Trust
ISPS Grant-in-Aid for Scientific Research
Projekt DEAL
Publisher
Springer Science and Business Media LLC
Subject
General Social Sciences,Philosophy
Reference122 articles.
1. Adams, R. A., Shipp, S., & Friston, K. J. (2013). Predictions not commands: Active inference in the motor system. Brain Structure and Function, 218(3), 611–643.
2. Anderson, B., & Moore, J. B. (1990). Optimal control: Linear quadratic methods. Upper Saddle River: Prentice-Hall Inc.
3. Anderson, M. L. (2017). Of Bayes and bullets: An embodied, situated, targeting-based account of predictive processing. Mainz: Johannes Gutenberg-Universität Mainz.
4. Baltieri, M., & Buckley, C. L. (2017). An active inference implementation of phototaxis. In Proceedings of the 14th European conference on artificial life 2017, Lyon, France, 4–8 September 2017.
5. Baltieri, M., & Buckley, C. L. (2018). The modularity of action and perception revisited using control theory and active inference. In Artificial life conference proceedings. MIT Press.
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