Houpt Joseph W, Bittner Jennifer L
Department of Psychology, Wright State University, Dayton, OH, United States.
711 HPW/RHCV, United States Air Force Research Laboratory, United States.
Vision Res. 2018 Jul;148:49-58. doi: 10.1016/j.visres.2018.04.004. Epub 2018 May 10.
Ideal observer analysis is a fundamental tool used widely in vision science for analyzing the efficiency with which a cognitive or perceptual system uses available information. The performance of an ideal observer provides a formal measure of the amount of information in a given experiment. The ratio of human to ideal performance is then used to compute efficiency, a construct that can be directly compared across experimental conditions while controlling for the differences due to the stimuli and/or task specific demands. In previous research using ideal observer analysis, the effects of varying experimental conditions on efficiency have been tested using ANOVAs and pairwise comparisons. In this work, we present a model that combines Bayesian estimates of psychometric functions with hierarchical logistic regression for inference about both unadjusted human performance metrics and efficiencies. Our approach improves upon the existing methods by constraining the statistical analysis using a standard model connecting stimulus intensity to human observer accuracy and by accounting for variability in the estimates of human and ideal observer performance scores. This allows for both individual and group level inferences.
理想观察者分析是视觉科学中广泛使用的一种基本工具，用于分析认知或感知系统利用可用信息的效率。理想观察者的表现为给定实验中的信息量提供了一种形式化度量。然后，将人类表现与理想表现的比率用于计算效率，这一结构可以在控制因刺激和/或任务特定需求导致的差异的同时，在不同实验条件下进行直接比较。在先前使用理想观察者分析的研究中，已使用方差分析和成对比较来测试不同实验条件对效率的影响。在这项工作中，我们提出了一个模型，该模型将心理测量函数的贝叶斯估计与分层逻辑回归相结合，用于推断未经调整的人类表现指标和效率。我们的方法通过使用将刺激强度与人类观察者准确性联系起来的标准模型来约束统计分析，并考虑人类和理想观察者表现分数估计中的变异性，从而改进了现有方法。这允许进行个体和群体水平的推断。
