Efficient curved reaches resulting from kinematic biases in the DIRECT model

The DIRECT model is a self-organizing neural network designed to explain neurophysiological and psychophysical data from targeted reaching experiments. The model´s learning process is indirectly influenced by the arm´s kinematics, resulting in movements biased toward joint rotations that produce the most spatial movement of the end-effector. This bias causes the end-effector trajectories performed after learning to deviate slightly from the straight path which would be produced by an explicit pseudoinverse computation, but the total joint rotation is significantly reduced by this slight curvature. A simplified model of this biasing is introduced, and implications regarding human arm movements are discussed