Cervantes Víctor H, Dzhafarov Ehtibar N
Department of Psychology, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA.
Department of Psychological Sciences, Purdue University, West Lafayette, IN 47907, USA.
Entropy (Basel). 2020 Sep 3;22(9):981. doi: 10.3390/e22090981.
This paper has two purposes. One is to demonstrate contextuality analysis of systems of epistemic random variables. The other is to evaluate the performance of a new, hierarchical version of the measure of (non)contextuality introduced in earlier publications. As objects of analysis we use impossible figures of the kind created by the Penroses and Escher. We make no assumptions as to how an impossible figure is perceived, taking it instead as a fixed physical object allowing one of several deterministic descriptions. Systems of epistemic random variables are obtained by probabilistically mixing these deterministic systems. This probabilistic mixture reflects our uncertainty or lack of knowledge rather than random variability in the frequentist sense.
本文有两个目的。一是展示认知随机变量系统的情境分析。另一个是评估早期出版物中引入的（非）情境性度量的新的分层版本的性能。作为分析对象，我们使用彭罗斯和埃舍尔所创作的那种不可能图形。我们不对不可能图形的感知方式做任何假设，而是将其视为一个固定的物理对象，允许有几种确定性描述中的一种。认知随机变量系统是通过对这些确定性系统进行概率混合得到的。这种概率混合反映的是我们的不确定性或知识的缺乏，而非频率主义意义上的随机变异性。
