Learning to select targets within targets in reaching tasks

We present a developmental neural network model of motor learning and control, called RL_SURE_REACH. In a childhood phase, a motor controller for goal directed reaching movements with a redundant arm develops unsupervised. In subsequent task-specific learning phases, the neural network acquires goal-modulation skills. These skills enable RL_SURE-REACH to master a task that was used in a psychological experiment by Trommershauser, Maloney, and Landy (2003). This task required participants to select aimpoints within targets that maximize the likelihood of hitting a rewarded target and minimizes the likelihood of accidentally hitting an adjacent penalty area. The neural network acquires the necessary skills by means of a reinforcement learning based modulation of the mapping from visual representations to the target representation of the motor controller. This mechanism enables the model to closely replicate the data from the original experiment. In conclusion, the effectiveness of learned actions can be significantly enhanced by fine-tuning action selection based on the combination of information about the statistical properties of the motor system with different environmental payoff scenarios.