Speed, Accuracy, and the Optimal Timing of Choices
将二元选择中决策概率与决策时间的联合分布建模为最优序贯抽样问题，发现当先验正确时快速决策更可能正确，比经典漂移扩散模型更好地拟合了决策时间与正确率的相关性。
We model the joint distribution of choice probabilities and decision times in binary decisions as the solution to a problem of optimal sequential sampling, where the agent is uncertain of the utility of each action and pays a constant cost per unit time for gathering information. We show that choices are more likely to be correct when the agent chooses to decide quickly, provided the agent’s prior beliefs are correct. This better matches the observed correlation between decision time and choice probability than does the classical drift-diffusion model (DDM), where the agent knows the utility difference between the choices.