Classic-probability accounts of mirrored (quantum-like) order effects in human judgments

David Kellen, Henrik Singmann, William H. Batchelder

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


Using a large data corpus, Wang, Solloway, Shiffrin, and Busemeyer (2014) showed that order effects in the responses given to pairs of related agree/disagree questions presented in succession follow a specific pattern termed QQ-equality. The fact that QQ-equality corresponds to a parameter-free prediction of a proposed quantumprobability model, together with the failure of several alternative classic-probability accounts, led Wang et al. to conclude that it constitutes strong evidence for the quantum nature of human judgments and to issue a challenge for the development of suitable classic-probability accounts. We respond to Wang et al.'s challenge by discussing a class of repeat-choice models that is able to yield the QQ-equality as a parameter-free prediction (or a very-likely prediction a priori) and provide an overall account of the data that is comparable to the quantum model. The success of this class of models establishes a plausible benchmark against which quantum accounts of order effects- like the ones observed in this data corpus-can be compared. Finally, we argue that the assumption of respondent homogeneity implied in Wang et al.'s use of aggregated data is extremely problematic for some of the alternative models discussed here (but not necessarily for the quantum account), leading to spurious rejections at non-negligible rates. We also discuss how a move away from aggregated data could help resolve some theoretical challenges that the quantum account of QQ-equality currently faces.

Original languageEnglish (US)
Pages (from-to)323-338
Number of pages16
Issue number4
StatePublished - Oct 2018


  • Human judgments
  • Order effects
  • Quantum probability

ASJC Scopus subject areas

  • Social Psychology
  • Neuropsychology and Physiological Psychology
  • Applied Psychology
  • Statistics, Probability and Uncertainty


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