Flexible neural encoding of dynamics allows precise tuning to the environment

To precisely control our movements, the sensorimotor control system builds representations of the external world. However, the neural encoding of these representations – whether we encode our control as a function of specific kinematic or kinetic variables – and how we might then use these representations to build up complex actions is still unknown. Each prior study has provided support for a different reference frame or proposed mixture models to explain the complex findings. Here, we propose a different framework in which the sensorimotor control system uses multiple representations, allowing it to flexibly tune its representations to task demands. We directly test this by showing that the representation of external forces is totally governed by the nature of the experienced forces. Participants form a global force representation based on the coordinate system that best explains the forces experienced at different arm postures. We suggest that when formalizing such global representation, the system decides on the representation by considering the level of uncertainty it has in each of the coordinate systems. This flexibility and redundancy in the representations allows the precise tuning to any specific task within our complex environment, explaining our expertise in a wide range of tasks.