Active inference unifies intentional and conflict-resolution imperatives of motor control
Fig 9
Third simulation: reaching under visuo-proprioceptive conflict that cannot be resolved by acting.
(A) Schematic representation of the agent seeing a virtual hand through a virtual reality stereoscopic headset. (B) Task specification. The agent has to reach a target placed at [xT, yT] = g(θT), while undergoing an illusory ownership over a virtual hand, which moves along the real hand but with a velocity gain of 1.3. (C-G) Dynamics of the model variables during the task. The vertical bars mark the time at which the target location is disclosed. (C) Joint angle of the real (solid black) and inferred (solid red) arm configurations, expressed in radians. The green line represents the desired arm configuration θT for which the hand is on the target. (D) Real (black) and inferred (red) velocity of the elbow joint angle. (E) Action, represented in our model as an angular acceleration. (F) The three prediction errors considered in the model: model dynamics error (green) and the two sensory errors, proprioceptive (magenta) and visual (blue). Please note that in this plot, the prediction error units correspond to different dimensions. (G) Contributions of proprioceptive (magenta) and visual (blue) errors in determining the action, more specifically the two addends in the r.h.s. of Eq G4 in Fig 6. (H) Real (solid curves) and inferred (dashed curves) velocities of the elbow joint angle for different simulations that use different values for the velocity gain, which maps the real elbow joint angle velocity into the corresponding join angle velocity of the virtual arm. The black line, corresponding to a velocity gain 1, is the same as in the first simulation. The inset permits appreciating the differences between the inferred velocities. See the main text for a more detailed explanation.