Bak Ji Hyun, Pillow Jonathan W
School of Computational Sciences, Korea Institute for Advanced Study, Seoul, Korea.
Department of Physics, Princeton University, Princeton, NJ, USA.
J Vis. 2018 Nov 1;18(12):4. doi: 10.1167/18.12.4.
Psychometric functions (PFs) quantify how external stimuli affect behavior, and they play an important role in building models of sensory and cognitive processes. Adaptive stimulus-selection methods seek to select stimuli that are maximally informative about the PF given data observed so far in an experiment and thereby reduce the number of trials required to estimate the PF. Here we develop new adaptive stimulus-selection methods for flexible PF models in tasks with two or more alternatives. We model the PF with a multinomial logistic regression mixture model that incorporates realistic aspects of psychophysical behavior, including lapses and multiple alternatives for the response. We propose an information-theoretic criterion for stimulus selection and develop computationally efficient methods for inference and stimulus selection based on adaptive Markov-chain Monte Carlo sampling. We apply these methods to data from macaque monkeys performing a multi-alternative motion-discrimination task and show in simulated experiments that our method can achieve a substantial speed-up over random designs. These advances will reduce the amount of data needed to build accurate models of multi-alternative PFs and can be extended to high-dimensional PFs that would be infeasible to characterize with standard methods.
心理测量函数（PFs）量化了外部刺激如何影响行为，并且它们在构建感觉和认知过程模型中起着重要作用。自适应刺激选择方法旨在根据实验中到目前为止观察到的数据，选择对PF具有最大信息量的刺激，从而减少估计PF所需的试验次数。在这里，我们针对具有两个或更多选项的任务中的灵活PF模型开发了新的自适应刺激选择方法。我们使用多项逻辑回归混合模型对PF进行建模，该模型纳入了心理物理行为的实际方面，包括失误和反应的多个选项。我们提出了一种用于刺激选择的信息理论标准，并基于自适应马尔可夫链蒙特卡罗采样开发了用于推理和刺激选择的计算高效方法。我们将这些方法应用于猕猴执行多选项运动辨别任务的数据，并在模拟实验中表明，我们的方法与随机设计相比可以实现大幅加速。这些进展将减少构建多选项PF准确模型所需的数据量，并且可以扩展到用标准方法难以表征的高维PF。
