Explaining and Predicting Question Order Effects Using a Quantum Probability Model

Monika Švogor

The order effect is a well-known phenomenon in cognitive psychology which violates specific axioms of the classical probability theory and refers to different probabilities of measurement outcomes (answers to questions) depending on their order. In this research, the order effect was analyzed using quantum probabilistic modeling of cognitive processes. The Quantum Question (QQ) model suggested by Wang and Busemeyer (2013) was tested on a sample of participants that had answered three yes/no questions about a painting, in different orders. For that purpose, three problems were considered. The first problem was to examine the presence of the order effect in participants’ answers and the results showed order effects for all three questions. The second problem was to test data fitness with a model prediction called QQ equality and the data proved to be a good fit to the model. The purpose of the third problem was to examine the dependency of the q value derived from the model on a third question. Given that the QQ equality is a prediction which includes only two yes/no questions, an additional prediction was derived from the model which includes a third question and according to which, the q value is independent of a third question. The results showed a limitation in the model in explaining empirical data. A proposal was made how to expand the theory, so it would include models with three or more questions.

Thomas Boyer‐Kassem, Sébastien Duchêne, Éric Guerci

Zheng Wang, Jerome R. Busemeyer

Question order effects are commonly observed in self-report measures of judgment and attitude. This article develops a quantum question order model (the QQ model) to account for four types of question order effects observed in literature. First, the postulates of the QQ model are presented. Second, an a priori, parameter-free, and precise prediction, called the QQ equality, is derived from these mathematical principles, and six empirical data sets are used to test the prediction. Third, a new index is derived from the model to measure similarity between questions. Fourth, we show that in contrast to the QQ model, Bayesian and Markov models do not generally satisfy the QQ equality and thus cannot account for the reported empirical data that support this equality. Finally, we describe the conditions under which order effects are predicted to occur, and we review a broader range of findings that are encompassed by these very same quantum principles. We conclude that quantum probability theory, initially invented to explain order effects on measurements in physics, appears to be a powerful natural explanation for order effects of self-report measures in social and behavioral sciences, too.

Diederik Aerts, Massimiliano Sassoli de Bianchi

Zheng Wang, Tyler Solloway, Richard M. Shiffrin +1 more

The hypothesis that human reasoning obeys the laws of quantum rather than classical probability has been used in recent years to explain a variety of seemingly "irrational" judgment and decision-making findings. This article provides independent evidence for this hypothesis based on an a priori prediction, called the quantum question (QQ) equality, concerning the effect of asking attitude questions successively in different orders. We empirically evaluated the predicted QQ equality using 70 national representative surveys and two laboratory experiments that manipulated question orders. Each national study contained 651-3,006 participants. The results provided strong support for the predicted QQ equality. These findings suggest that quantum probability theory, initially invented to explain noncommutativity of measurements in physics, provides a simple account for a surprising regularity regarding measurement order effects in social and behavioral science.

Cong Wang, Yunxia Chen, Kunsong Lin +1 more