Psychological Sciences, University of Melbourne, Victoria, Australia.
Neural Comput. 2011 Aug;23(8):2000-31. doi: 10.1162/NECO_a_00150. Epub 2011 Apr 26.
An important class of psychological models of decision making assumes that evidence is accumulated by a diffusion process to a response criterion. These models have successfully accounted for reaction time (RT) distributions and choice probabilities from a wide variety of experimental tasks. An outstanding theoretical problem is how the integration process that underlies diffusive evidence accumulation can be realized neurally. Wang ( 2001 , 2002 ) has suggested that long timescale neural integration may be implemented by persistent activity in reverberation loops. We analyze a simple recurrent decision making architecture and show that it leads to a diffusive accumulation process. The process has the form of a time-inhomogeneous Ornstein-Uhlenbeck velocity process with linearly increasing drift and diffusion coefficients. The resulting model predicts RT distributions and choice probabilities that closely approximate those found in behavioral data.
一类重要的决策心理模型假设，证据是通过扩散过程积累到一个反应标准的。这些模型成功地解释了来自各种实验任务的反应时间（RT）分布和选择概率。一个悬而未决的理论问题是，扩散证据积累所基于的整合过程如何在神经上实现。Wang（2001，2002）提出，长时间尺度的神经整合可能是通过回荡回路中的持续活动来实现的。我们分析了一个简单的递归决策模型，并表明它导致了一个扩散积累过程。该过程的形式是具有线性增加漂移和扩散系数的时变非齐次 Ornstein-Uhlenbeck 速度过程。由此产生的模型预测了 RT 分布和选择概率，这些概率与行为数据中发现的概率非常接近。
