Decision-time statistics of nonlinear diffusion models: characterizing long sequences of subsequent trials (Q826929)

The paper reports on one alternative approach to modeling binary decision making in the diffusion decision model. The authors introduce decision trains, sequence of negative or positive spikes at the decision times The signs of the spikes reflect correctness or incorrectness of the decision. Proper characterization of experiments are established using the decision trains. Relations between the second-order statistics of the decision trains and the response-time densities are derived. Obtained results are compared with direct simulations of the stochastic model. Outlook to further research in the area is placed at the end of the paper.