时变扰动可以区分反应时任务中知觉决策的积分阈值模型
Time-varying perturbations can distinguish among integrate-to-threshold models for perceptual decision-making in reaction time tasks
摘要：
几个集成到阈值模型与不同的时间整合机制已被提出来描述的感官证据的积累，以规定的水平之前，运动反应的知觉决策任务。实验和模拟研究表明，在积分过程中引入时变扰动可以区分这些模型中的一些。在这里，我们提出了计算机模拟和数学证明，提供更严格的比较一维随机微分方程模型。使用两个扰动协议，并专注于所产生的变化的手段和标准偏差的决策时间，我们表明，对于高信噪比，漂移扩散模型与常数和随时间变化的漂移率可以区分从Ornstein-Uhlenbeck过程，但不一定彼此。该协议也可以区分稳定的不稳定的Ornstein-Uhlenbeck过程，我们表明，非线性积分器可以区分这些线性模型的标准偏差的变化。该协议可以在行为实验中实现。
英文摘要：
Several integrate-to-threshold models with differing temporal integration mechanisms have been proposed to describe the accumulation of sensory evidence to a prescribed level prior to motor response in perceptual decision-making tasks. An experiment and simulation studies have shown that the introduction of time-varying perturbations during integration may distinguish among some of these models. Here, we present computer simulations and mathematical proofs that provide more rigorous comparisons among one-dimensional stochastic differential equation models. Using two perturbation protocols and focusing on the resulting changes in the means and standard deviations of decision times, we show that, for high signal-to-noise ratios, drift-diffusion models with constant and time-varying drift rates can be distinguished from Ornstein-Uhlenbeck processes, but not necessarily from each other. The protocols can also distinguish stable from unstable Ornstein-Uhlenbeck processes, and we show that a nonlinear integrator can be distinguished from these linear models by changes in standard deviations. The protocols can be implemented in behavioral experiments.
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