• Probabilistic Conditional Reasoning

    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.

  • The Relevance Effect and Conditionals

    Skovgaard-Olsen, N., Singmann, H., & Klauer, K. C. (2016). The relevance effect and conditionals. Cognition, 150, 26–36.

  • ROC residuals in SDT models

    Kellen, D., & Singmann, H. (2016). ROC residuals in signal-detection models of recognition memory. Psychonomic Bulletin & Review, 23, 253-264.

  • Algorithmic Complexity for Psychology

    Gauvrit, N., Singmann, H., Soler-Toscano, F., & Zenil, H. (in press). Algorithmic complexity for psychology: a user-friendly implementation of the coding theorem method. Behavior Research Methods.

Paper with Niels Skovgaard-Olsen and Christoph Klauer accepted at

Cognitive Science


  • 2htm

    MPT models

    Multinomial Processing Tree (MPT) models are cognitive measurement models for categorical data. They describe observed response frequencies from a finite set of response categories (i.e., responses following a multinomial distribution) with a finite number of latent states. Each latent state is reached by particular combinations of cognitive processes; processes that are assumed to take place in an all-or-nothing fashion.

  • conditional


    Reasoning is the ability to infer propositions from given propositions. The psychological research is concerned with the cognitive processes underlying human reasoning. An influential way of addressing this question has been to compare observed reasoning performance with normative systems such as bivalent logic or probability theory with the goal to infer that underlying processes somehow mimic or reproduce normative systems.

  • reco_models

    Recognition Memory Models

    Recognition memory is concerned with the ability to discriminate between previously encountered information and new information. A central question is how to disentangle response tendencies (e.g., the tendency to respond "old") from memory performance (i.e., the ability to discriminate between old and new information). Several measurement models with markedly different assumptions about the underlying memory process exist.

  • lmm_example

    Mixed Models

    Mixed models (aka multilevel or hierarchical models) are statistical models containing both fixed- and random-effects terms. They are useful when multiple measurements exist for each unit of observation (e.g., participant or item), or for hierarchical data. Linear mixed models (LMMs) are used for normally distributed dependent variables, generalized linear mixed models (GLMMs) are applicable to other distributions (e.g., binomial data).