Department of Psychological and Brain Sciences, Indiana University.
School of Communication, The Ohio State University.
Psychol Rev. 2018 Jul;125(4):572-591. doi: 10.1037/rev0000106.
A general theory of measurement context effects, called Hilbert space multidimensional (HSM) theory, is presented. A measurement context refers to a subset of psychological variables that an individual evaluates on a particular occasion. Different contexts are formed by evaluating different but possibly overlapping subsets of variables. Context effects occur when the judgments across contexts cannot be derived from a single joint probability distribution over the complete set of values of the observed variables. HSM theory provides a way to model these context effects by using quantum probability theory, which represents all the variables within a low dimensional vector space. HSM models produce parameter estimates that provide a simple and informative interpretation of the complex collection of judgments across contexts. Comparisons of HSM model fits with Bayesian network model fits are reported for a new large experiment, demonstrating the viability of this new model. We conclude that the theory is broadly applicable to measurement context effects found in the social and behavioral sciences. (PsycINFO Database Record
一种名为希尔伯特空间多维(HSM)理论的通用测量上下文效应理论被提出。测量上下文是指个体在特定场合评估的心理变量的子集。不同的上下文是通过评估不同但可能重叠的变量子集形成的。当跨上下文的判断不能从观察变量的完整集合的单个联合概率分布中推导出来时,就会出现上下文效应。HSM 理论通过使用量子概率论为这些上下文效应提供了一种建模方法,量子概率论将所有变量表示在低维向量空间内。HSM 模型产生的参数估计值为跨上下文的复杂判断提供了简单而有信息的解释。对一个新的大型实验进行了 HSM 模型拟合与贝叶斯网络模型拟合的比较,证明了这种新模型的可行性。我们的结论是,该理论广泛适用于社会和行为科学中发现的测量上下文效应。