Selectivity in probabilistic causality: Where psychology runs into quantum physics

详细信息	查看全文 | 推荐本文 |

• 作者：Ehtibar N. Dzhafarov^a ; ^{ehtibar@purdue.edu} ; Janne V. Kujala^b
• 关键词：Bell-type inequalities ; EPR paradigm ; Factorial design ; Fine&rsquo ; s inequalities ; Joint distribution criterion ; Probabilistic causality ; Mental architectures ; Random outputs ; Selective influences ; Quantum entanglement ; Thurstonian scaling
• 刊名：Journal of Mathematical Psychology
• 出版年：2012
• 期刊代码：137_00222496
• 类别：cp
• 出版时间：February, 2012
• 卷：56
• 期：1
• 页码：54-63
• 文件大小：498 K

摘要

Given a set of several inputs into a system (e.g., independent variables characterizing stimuli) and a set of several stochastically non-independent outputs (e.g., random variables describing different aspects of responses), how can one determine, for each of the outputs, which of the inputs it is influenced by? The problem has applications ranging from modeling pairwise comparisons to reconstructing mental processing architectures to conjoint testing. A necessary and sufficient condition for a given pattern of selective influences is provided by the Joint Distribution Criterion, according to which the problem of 鈥渨hat influences what鈥?is equivalent to that of the existence of a joint distribution for a certain set of random variables. For inputs and outputs with finite sets of values this criterion translates into a test of consistency of a certain system of linear equations and inequalities (Linear Feasibility Test) which can be performed by means of linear programming. While new in the behavioral context, both this test and the Joint Distribution Criterion on which it is based have been previously proposed in quantum physics, in dealing with generalizations of Bell inequalities for the quantum entanglement problem. The parallels between this problem and that of selective influences in behavioral sciences are established by observing that noncommuting measurements in quantum physics are mutually exclusive and can therefore be treated as different levels of one and the same factor.