I’m a postdoctoral researcher in the Psychological Methods group at the University of Amsterdam.
I develop and test mathematical models of human cognition using Bayesian statistics and try to improve the computational reproducibility of psychological science.
I also create R packages.
Bartoš, F., Aust, F., & Haaf, J. M. (2022). Informed Bayesian survival analysis. BMC Medical Research Methodology, 22, 238.
van Doorn, J., Haaf, J.M., Stefan, A.M., Wagenmakers, E.-J., Cox, G.E., Davis-Stober, C.P., Heathcote, A., Heck, D., Kalish, M., Kellen, D., Matzke, D., Morey, R.D., Nicenboim, B., van Ravenzwaaij, D., Rouder, J.N., Shiffrin, R.M., Schad, D.J., Singmann, H., Vasishth, S., Verissimo, J., Chandramouli, S., Dunn, J.C., Gronau, Q.F., Navarro, D., Yadav, H., Bockting, F., Linde, M., McMullin, S.D., Schnuerch, M., & Aust. F. (2023). Bayes factors for mixed models: A discussion. Computational Brain & Behavior, 6, 140–158.
Sarafoglou, A., Aust, F., Marsman, M., Wagenmakers, E.-J., & Haaf, J. M. (submitted). multibridge: An R package to evaluate informed hypotheses in Binomial and Multinomial models. PsyArXiv.
van Doorn, J., Aust, F., Haaf, J. M., Stefan, A. M., & Wagenmakers, E.-J. (in press). Bayes Factors for Mixed Models. Computational Brain & Behavior.
Aust, F., Haaf, J. M., & Stahl, C. (2019). A memory-based judgment account of expectancy-liking dissociations in evaluative conditioning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 45(3), 417-439. [OSF project]
Rouder, J. N., Haaf, J. M., & Aust, F. (2018). From theories to models to predictions: A Bayesian model comparison approach. Communication Monographs, 85(1), 41-56.
