Figure 1. Neural network implementation of the DDM and associated LRP predictions.

The neural network model of decision making consists of three layers (units with S-shaped curves denote bistable switch units). In the evidence accumulation layer (A), located potentially in parietal cortex, activity increases with motion input over time (red = leftward motion; blue = rightward motion). The difference in left minus right activations approximates a drift-diffusion process. This accumulation layer feeds into the threshold layer (B), which is potentially located in primary motor cortex. The difference between left and right switch unit activations then reflects the LRP signal. The threshold layer's output is transformed into a punctate motor output in the response layer (A), which also ensures that the threshold and accumulation layers are reset after the response. (D) Our model predicts that for changes in signal-to-noise ratio, in the accumulation layer the slope of neural activity changes, while in the threshold layer there is a non-linear change in slope. Consequently, the change due to increases in signal-to-noise ratio is best quantified by the area between curves. (E) Additionally, the model predicts that the magnitude and location of the response-locked simulated LRP should vary with signal-to-noise ratio (pink = low drift;green = high drift). (F) When varying response bias, the model predicts that the height of the LRP should change (pink = low bias;green = high bias).