Predicting explorative motor learning using decision-making and motor noise
Fig 6
An illustration of the model for the decision-making task and the explorative motor learning task.
On each trial, a hidden target is chosen (Environment). That is, the environment is in a state, which is not directly observable. The model starts with an initial uniformly distributed belief state (illustrated with the red arrow on the top right). On each time step, given an belief, the model then chooses an action based on the belief-action value function (Action selection). Subsequently, the action is executed (Execution). Decision-making task actions are performed without motor noise; the model is able to choose the selected action accurately. Reaching actions are performed with motor noise; there is uncertainty between the selected and executed action. Once the action is executed, the environment gives observable feedback (ot−1 = 35 in the figure). The action and observation are then used to update the belief (Bayesian belief update). The update is constrained by the fact that participants were naïve to the score function used. We modelled this uncertainty using the likelihood uncertainty parameter (Γ; Eq 3). A new cycle then starts with the new belief state (Bt).