Adaptive psychophysical procedures aim to increase the efficiency and reliability of measurements. With increasing stimulus and experiment complexity in the last decade, estimating multi-dimensional psychometric functions has become a challenging task for adaptive procedures. If the experimenter has limited information about the underlying psychometric function, it is not possible to use parametric techniques developed for the multi-dimensional stimulus space. Although there are non-parametric approaches that use Gaussian process methods and specific hand-crafted acquisition functions, their performance is sensitive to proper selection of the kernel function, which is not always straightforward. In this work, we use a neural network as the psychometric function estimator and introduce a novel acquisition function for stimulus selection. We thoroughly benchmark our technique both using simulations and by conducting psychovisual experiments under realistic conditions. We show that our method outperforms the state of the art without the need to select a kernel function and significantly reduces the experiment duration.

翻译：自适应心理物理程序旨在提高测量的效率和可靠性。随着过去十年间刺激与实验复杂度的增加，估计多维心理测量函数已成为自适应程序面临的一项挑战性任务。若实验者对潜在的心理测量函数了解有限，则无法使用为多维刺激空间开发的参数化技术。尽管存在使用高斯过程方法和特定手工设计采集函数的非参数方法，但其性能对核函数的恰当选择十分敏感，而这一选择往往并非易事。本研究利用神经网络作为心理测量函数估计器，并提出了一种用于刺激选择的新型采集函数。我们通过仿真实验及在真实条件下进行心理视觉实验，全面评估了该技术。结果表明，我们的方法无需选择核函数即可超越当前最先进水平，并显著缩短实验时长。