Figure 6.

Example circuit motifs proposed to model decision-making and its flexible adjustments. S1 and S2 are inputs from sensory neurons selective to the discriminated stimuli (e.g., left and right motion directions). A1 and A2 are drives for the two actions (e.g., leftward and rightward saccade). (a) When a decision is made through competition of choice-selective neural modules, changes in self-excitation (red plus signs) alter speed-accuracy trade-off. (b) To achieve flexible stimulus-action mapping, a control module could switch the routing of sensory information depending on contextual signals. (c) More recent models implement similar computations by training recurrent neural networks. (d) Embracing the distributed nature of neural processing and interactions in the actual brain can yield mechanistic models that better explain the neural responses. Multi-module RNNs are a fruitful step in that direction.