Dynamic Integration of Value Information into a Common Probability Currency as a Theory for Flexible Decision Making
Fig 5
Saccadic movements in tasks with competing targets.
A: Simulated saccadic movements for pair of targets with 30° (gray traces) and 90° (black traces) target separation. B: A method followed to visualize the relative desirability function of two competing saccadic policies (see results section for more details). C: Heat map of the relative desirability function at different states to saccade to the left target, at a 30° target separation. Red and blue regions corresponds to high and low desirability states, respectively. Black traces correspond to averaged trajectories in single-target trials. Notice the strong competition between the two saccadic policies (greenish areas). D: Similar to panel C, but for 90° target separation. In this case, targets are located in areas with no competition between the two policies (red and blue regions). E: Examples of saccadic movements (left column) with the corresponding time course of the relative desirability of the two policies (right column). The first two rows illustrate characteristic examples from 30° target separation, in which competition results primarily in saccade averaging (top panel) and less frequently in correct movements (middle panel). The bottom row shows a characteristic example from 90° target separation, in which the competition is resolved almost immediately after saccadic onset, producing almost no errors. F: Percentage of simulated averaging saccades for different degrees of target separation (red line)—green, blue and cyan lines describe the percentage of averaging saccades performed by 3 monkeys [24].