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Continuous evaluation of cost-to-go for flexible reaching control and online decisions

Fig 7

Model implementation.

A Schematic representation of the computation of online changes in task demands. At each time step, the time-varying task parameters Θ_t are used to derive the optimal control policy that is used to compute the motor command u_t (black arrow) which depends on the dynamical state estimate (dotted arrow) computed through dynamical Bayesian integration (state estimation). B Schematic representation of the implementation of online motor decisions in a three targets paradigm (see panel C). Each line schematized the time-varying cost-to-go functions associated with each option which are compared such that the one associated with the lowest value (see panel D) is selected (represented by the filled rectangle) and the corresponding motor command (full black arrow) is applied to the system for that very time step. C Representation of the different targets and an exemplar simulated hand trajectory (exaggerated case for illustration), the dots correspond to the time at which the decisions processes were considered. D Cost-to-go functions associated with the three targets evaluated at three different time points. The filled dots represent the minimum values that instructed the decision process, whenever these filled dots fall on a new color, it corresponds to an online change in target.

doi: https://doi.org/10.1371/journal.pcbi.1011493.g007