Fig 5. Model of reaching and target selection.

Top row: model of reaching. A: On each trial, the model maintains an estimate of the reach perturbation (blue curve) for all target directions. The reach angle counteracts the expected perturbation in the selected target direction (blue dot). B: After movement, the observed perturbation (red dot) is used to calculate the model prediction error (difference between the observed and predicted perturbation, purple). C: The model updates the reach perturbations using the prediction error. The dashed curve shows the previous estimate and the solid curve shows the updated estimate. D: Generalization of the learning is controlled by a kernel function (orange) centered on the selected target direction. Bottom row: model of target selection. E: The model maintains the expected loss (absolute feedback error) as a function of target direction. The probability of selecting each potential target (green numbers) is calculated by applying a softmax function to the expected loss values of the three targets. F: After movement, the observed loss is used to calculate the model prediction error (difference between the observed and predicted loss, purple). G: The model updates the loss function using the prediction error. H: Amount of the loss function update in different directions is controlled by a kernel function (orange) centered on the selected target direction.