A quantum probability account of individual differences in causal reasoning

Publication date: December 2018Source: Journal of Mathematical Psychology, Volume 87Author(s): Percy K. Mistry, Emmanuel M. Pothos, Joachim Vandekerckhove, Jennifer S. TruebloodAbstractWe use quantum probability (QP) theory to investigate individual differences in causal reasoning. By analyzing datasets from Rehder (2014) on comparative judgments, and from Rehder and Waldmann (2016) on absolute judgments, we show that a QP model can both account for individual differences in causal judgments, and why these judgments sometimes violate the properties of causal Bayes nets. We implement this and previously proposed models of causal reasoning (including classical probability models) within the same hierarchical Bayesian inferential framework to provide a detailed comparison between these models, including computing Bayes factors. Analysis of the inferred parameters of the QP model illustrates how these can be interpreted in terms of putative cognitive mechanisms of causal reasoning. Additionally, we implement a latent classification mechanism that identifies subcategories of reasoners based on properties of the inferred cognitive process, rather than post hoc clustering. The QP model also provides a parsimonious explanation for aggregate behavior, which alternatively can only be explained by a mixture of multiple existing models. Investigating individual differences through the lens of a QP model reveals simple but strong alternatives to existing explanations for the dichotomies o...
Source: Journal of Mathematical Psychology - Category: Psychiatry & Psychology Source Type: research
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