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Henrik Singmann (Assistant Professor)

Henrik Singmann



My primary research interests concern the cognitive processes underlying memory, reasoning, judgements, and decision making. In these domains, I am working towards developing formal theories that are able to describe the computational processes on an individual level.

In addition, I am interested in the development and implementation of computational methods and software tools for psychology and other empirical disciplines. Most of this work is done using the statistical programming language R. Recently, I am becoming more and more interested in Bayesian statistical methods, primarily using Stan.

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  • Trippas, D., Kellen, D., Singmann, H., Pennycook, G., Koehler, D. J., Fugelsang, J. A., & DubĂ©, C. (2018). Characterizing belief bias in syllogistic reasoning: A hierarchical Bayesian meta-analysis of ROC data. Psychonomic Bulletin & Review.

  • Singmann, H., Klauer, K. C., & Beller, S. (2016). Probabilistic conditional reasoning: Disentangling form and content with the dual-source model. Cognitive Psychology, 88, 61–87. 

  • Singmann, H., Klauer, K. C., & Over, D. E. (2014). New normative standards of conditional reasoning and the dual-source model. Frontiers in
    Psychology, 5, 316. 

  • Singmann, H., & Kellen, D. (2013). MPTinR: Analysis of multinomial processing tree models in R. Behavior Research Methods, 45(2), 560–575.

  • Kellen, D., Klauer, K. C., & Singmann, H. (2012). On the measurement of criterion noise in signal detection theory: The case of recognition memory. Psychological Review, 119(3), 457–479.