A recurrent neural network framework for flexible and adaptive decision making based on sequence learning
Fig 6
a. The connection weights between the eye movement output units and the when units (upper panel) and the which units (lower panel). b. The reaction time of the network choice when the outputs of different groups of units are inactivated. c. Lesions to the when and which units affect choices differently. The blue bars indicate the proportion of correct trials. The orange bars indicate the proportion of trials in which the choice is consistent with the sign of the accumulated evidence at the time of choice. The green bars indicate the percentage of trials in which the model chooses the left target. d. Speed-accuracy tradeoff. We suppress the output of a different proportion of +when/-when units (see Methods). As more +when units’ outputs are suppressed, the model’s reaction time (black curve, right y-axis) increases along with the accuracy (blue curve, left y-axis). However, the proportion of trials in which the choices are consistent with the evidence (orange curve, left y-axis) stays the same except for the extreme cases. e. The maximum flow (upper panel) and the inverse of the geodesic distance (lower panel) between different unit groups. The smaller maximum flow and the larger geodesic distance between when/which units and other units suggest the relatively tight connections between the when and which units. ※ indicates a significant difference (p<0.05, Two-tailed t-test with Bonferroni correction). The error bars in all panels indicate the SE across runs.