Assistant professor at the University of Amsterdam

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I am an assistant professor at the Psychological Methods Unit of the University of Amsterdam. My research focuses on Bayesian hierarchical modeling of cognitive phenomena such as interference effects (Does everyone Stroop?), recognition memory, and subliminal priming. I am mainly interested in individual differences in cognitive tasks and how to disentangle stable, qualitative individual differences from sample noise.

Even though a large part of my research is developing statistical models, I am an experimental psychologist at heart, and I get excited about innovative, bold experimental designs. But I also believe that statistics are important to express and test psychological theory. And that Bayesian statistics are better suited to do so than conventional t-tests or ANOVA. My work particularly focuses on the usefulness of ordinal constraints in modeling. It seems that ordinal constraints are particularly well suited to capture theoretical predictions in psychology.

Before my current position I was a postdoctoral research fellow working with EJ Wagenmakers at the Department of Psychological Methods (University of Amsterdam). Prior to that I earned my PhD in quantitative psychology (2018) from the University of Missouri working with Jeff Rouder.

Throughout my research, I try to be transparent and provide data, code, and open access to manuscripts I wrote. Click here for a list of publications.

And this is Frank, my cat.



Aust, F., van Doorn, J., & Haaf, J. M. (2022). Translating default priors from linear mixed models to repeated-measures ANOVA and paired t-tests. PsyArXiv

Haaf, J. M., Klaassen, F., & Rouder, J.N. (2018). Capturing Ordinal Theoretical Constraint in Psychological Science. PsyArXiv

Haaf, J. M., Klaassen, F., & Rouder, J.N. (under review). Bayes factor vs. Posterior-Predictive Model Assessment: Insights from Ordinal Constraints. PsyArXiv

Ramotowska, S., Haaf, J. M., Van Maanen, L., & Szymanik, J. (under review). Most Quantifiers Have Many Meanings. Semanticsarchive

Rouder, J. N., & Haaf, J. M. (under review). Optional Stopping and the Interpretation of The Bayes Factor. PsyArXiv

Sánchez-Fuenzalida, N., van Gaal, S., Fleming, S., Haaf, J. M., & Fahrenfort, J. J. (under review). Predictions and rewards affect decision making but not subjective experience. PsyArXiv

Sarafoglou, A., Kuhlmann, B. G., Aust, F., & Haaf, J. M. (under review). Theory-Informed Refinement of Bayesian Hierarchical MPT Modeling. PsyArXiv

Veenman, M., Stefan, A., & Haaf, J. M. (under review). Bayesian Hierarchical Modeling: An Introduction and Reassessment. PsyArXiv

In Press

Berkhout, S. W., Haaf, J. M., Gronau, Q. F., Heck, D. W., & Wagenmakers, E. (in press). A Tutorial on Bayesian Model-Averaged Meta-Analysis in JASP. Behavior Research Methods PsyArXiv

Hoogeveen, S., Berkhout, S. W., Gronau, Q. F., Wagenmakers, E., & Haaf, J. M. (in press). Improving Statistical Analysis in Team Science: The Case of a Bayesian Multiverse of Many Labs 4, Advances in Methods and Practices in Psychological Science. PsyArXiv

Rouder, J. N., Kumar, A., & Haaf, J. M. (in press). Why Most Studies of Individual Differences With Inhibition Tasks Are Bound To Fail. Psychonomic Bulletin and Review PsyArXiv

Sarafoglou, A., Aust, F., Marsman, M., Wagenmakers, E.-J., & Haaf, J. M. (in press). multibridge: An R package to evaluate informed hypotheses in Binomial and Multinomial models. Behavior Research Methods PsyArXiv

Sarafoglou, A., Bartoš, F., Stefan, A. M., Haaf, J. M., & Wagenmakers, E. (in press). “This Behavior Strikes us as Ideal”: Assessment and Anticipations of Huisman (2022). Psychonomic Bulletin & Review.

Sarafoglou, A., Haaf, J. M., Ly, A., Gronau, Q. F., Wagenmakers, E., & Marsman, M. (in press). Evaluating Multinomial Order Restrictions with Bridge Sampling. Psychological Methods PDF


Donzallaz, M., Haaf, J. M., & Stevenson, C. (2023). Creative or Not? Hierarchical Diffusion Modeling of the Creative Evaluation Process. Journal of Experimental Psychology: Learning, Memory, and Cognition, 49(6), 849–865. PDF

van Doorn, J., Aust, F., Haaf, J. M., Stefan, A., & Wagenmakers, E. J. (2023). Bayes Factors for Mixed Models: Perspective on Responses. Computational Brain & Behavior, 6(1), 127-139.

van Doorn, J., Haaf, J. M., Stefan, A. M., Wagenmakers, E., Cox, G. E., Davis-Stober, C. P., … Aust, F. (2023). Bayes Factors for Mixed Models: A Discussion. Computational Brain & Behavior, 6(1), 140-158.

Haaf, J. M., & Rouder, J. N. (2023). Does Every Study? Implementing Ordinal Constraint in Meta-Analysis. Psychological Methods, 28(2), 472–487. PDF


Bartoš, F., Aust, F., & Haaf, J. M. (2022). Informed Bayesian survival analysis. BMC Medical Research Methodology, 22, 238. doi:0.1186/s12874-022-01676-9. PDF

Van Geert, E., Moors, P., Haaf, J. M., & Wagemans, J. (accepted first-stage registered report). Same Stimulus, Same Temporal Context, Different Percept? Individual Differences in Hysteresis and Adaptation When Perceiving Multistable Dot Lattices. Stage 1 registered report. i-Perception, 13(4), 20416695221109300. PDF

Hoogeveen, S., Haaf, J. M., Bulbulia, J. A., Ross, R. M., McKay, R., Altay, S., ... & van Elk, M. (2022). The Einstein effect provides global evidence for scientific source credibility effects and the influence of religiosity. Nature Human Behaviour, 6(4), 523-535. PDF

Rouder, J. N., Schnuerch, M., Haaf, J. M., & Morey, R. D. (2022). Principles of Model Specification in ANOVA Designs. Computational Brain & Behavior, 1-14.

Schnuerch, M., Haaf, J. M., Sarafoglou, A., & Rouder, J. N. (2022). Meaningful comparisons with ordinal-scale items. Collabra: Psychology, 8(1), 38594. PDF


van den Bergh, D., Haaf, J. M., Ly, A., Rouder, J. N., & Wagenmakers, E. J. (2021). A cautionary note on estimating effect size. Advances in Methods and Practices in Psychological Science, 4(1), 1-15. link

van Doorn, J., Aust, F., Haaf, J. M., Stefan, A. M., & Wagenmakers, E.-J. (2021). Bayes Factors for Mixed Models. Computational Brain & Behavior, 1-13. Computational Brain & Behavior. PDF

van Doorn, J., van den Bergh, D., Boehm, U., Dablander, F., Derks, K., Draws, T., Evans, N.J., Gronau, Q. F., Haaf, J. M., Hinne, M., Kucharsky, S., Ly, A., Marsman, M., Matzke, D., Raj, A.K.N., Sarafoglou, A., Stefan, A., Voelkel, A.G., & Wagenmakers, E.-J. (2021) The JASP guidelines for conducting and reporting a Bayesian analysis. Psychonomic Bulletin and Review, 28, 813–826. PDF

van Doorn, J., van den Bergh, D., Dablander, F., van Dongen, N., Derks, K., Evans, N., Gronau, Q., Haaf, J. M., Kunisato, Y., Ly, A., Marsman, M., ºSarafoglou, A., Stefan, A., & Wagenmakers, E. (2021). Strong Public Claims May Not Reflect Researchers’ Private Convictions. Significance, 18, 44-45.

Gronau, Q. F., Heck, D., Berkhout, S., Haaf, J. M., & Wagenmakers, E. (2021). A Primer on Bayesian Model-Averaged Meta-Analysis. Advances in Methods and Practice in Psychological Science, 4(3), 25152459211031256. PDF

Haaf, J. M., Rhodes, S., Naveh-Benjamin, M., Sun, T. K., Snyder, H. K., & Rouder, J. N. (2021). Revisiting the Remember-Know Task: Replications of Gardiner and Java (1990). Memory & Cognition, 49, 46-66. OSF project & PDF

Rouder, J. N., & Haaf, J. M. (2021). Are There Reliable Qualitative Individual Difference in Cognition? Journal of Cognition, 4(1), 46. PDF

Tierney, W., Hardy, J. H., III., Ebersole, C., Viganola, D., Clemente, E., Gordon, M., Hoogeveen, S., Haaf, J., Dreber, A.A., Johannesson, M., Pfeiffer, T., Chapman, H., Gantman, A., Vanaman, M., DeMarree, K., Igou, E., Wylie, J., Storbeck J., Andreychik, M.R., McPhetres, J., Vaughn, L.A., Culture and Work Morality Forecasting Collaboration, & Uhlmann, E. L. (2021). A creative destruction approach to replication: Implicit work and sex morality across cultures. Journal of Experimental Social Psychology, 93, 104060. link


Haaf, J. M., Merkle, E. C., & Rouder, J. N. (2020). Do items order? The psychology in IRT models. Journal of Mathematical Psychology, 98, 102398. PDF


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. htp:// . PDF & OSF project

Haaf, J. M., Ly, A., & Wagenmakers, E.-J. (2019). Retire significance, but still test hypotheses. Nature, 567, 461.

Haaf, J. M., & Rouder, J. N. (2019). Some do and some don’t? Accounting for variability of individual difference structures. Psychonomic Bulletin and Review, 26(3), 772-789. PDF

Rouder, J. N., & Haaf, J. M. (2019). A Psychometrics of Individual Differences in Experimental Tasks. Psychonomic Bulletin and Review, 26(2), 452-467. PDF

Rouder, J. N., Haaf, J. M., Davis-Stober, C., & Hilgard, J. (2019). Beyond overall effects: A Bayesian approach to finding constraints across a collection of studies in meta-analysis. Psychological Methods. PDF

Rouder, J. N., Haaf, J. M., & Snyder, H. K. (2019). Minimizing Mistakes In Psychological Science. Advances in Methods and Practices in Psychological Science, 2(1), 3-11. PDF

Snyder, H. K., Rafferty, S. M., Haaf, J. M., & Rouder, J. N. (2019). Common or Distinct Attention Mechanisms for Contrast and Assimilation. Attention, Perception, & Psychophysics, 81(6). 1944-1950. PDF


Etz, A., Haaf, J. M., Rouder, J. N., & Vandekerckhove, J. (2018). Bayesian inference and testing any hypothesis you can specify. Advances in Methods and Practices in Psychological Science, 1(2), 281-295. PDF & OSF project (with app)

Heycke, T., Gehrmann, S. M., Haaf, J., & Stahl, C. (2018). Of two minds or one? A registered replication of Rydell et al. (2006). Emotion and Cognition, 32(8). PDF & OSF project

Rouder, J. N., Haaf, J. M., & Vandekerckhove, J. (2018). Bayesian Inference in Psychology, Part IV: Parameter estimation and Bayes factors. Psychonomic Bulletin and Review, 25(1), 102-113. 10.3758/s13423-017-1420-7. PDF

Rouder, J., & Haaf, J. (2018). Power, Dominance, and Constraint: A Note on the Appeal of Different Design Traditions. Advances in Methods and Practice in Psychological Science, 1(1), 19-26. PDF Github

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. 10.1080/03637751.2017.1394581. PDF


Haaf, J. M., & Rouder, J. N. (2017). Developing Constraint in Bayesian Mixed Models. Psychological Methods, 22(4), 779-798. PDF Github

Thiele, J. E., Haaf, J. M., & Rouder, J. N. (2017). Is there variation across individuals in processing? Bayesian analysis for systems factorial technology. Journal of Mathematical Psychology, 81, 40-54. 10.1016/ PDF Github


Stahl, C., Haaf, J., & Corneille, O. (2016). Subliminal evaluative conditioning? Above-chance CS identification may be necessary and insufficient for attitude learning. Journal of Experimental Psychology: General, 145(9), 1107-1131. /10.1037/xge0000191. PDF Github



Upcoming Talks and Workshops

Some Recent Invited Talks and Workshops

Invite Me! I enjoy giving workshops and talks. If you would like to invite me to your university, contact me. My expertise is in Bayesian hierarchical modeling, individual differences, meta-analysis, and reproducible coding in R.


Lab Meetings

The Amsterdam Mathematical Psychology Lab organizes biweekly lab meetings at the Psych Methods group to discuss topics around mathematical psychology, Bayesian modeling and computational modeling. The lab meeting is currently open to collaborators, PhD students and master students. If you would like to join on a regular basis shoot me an email!



Julia Haaf
Psychological Methods
Nieuwe Achtergracht 129-B | Room G0.34
University of Amsterdam

New Email: j.m.haaf [at] uva [dot] nl

If you are interested in working with me on a project I am happy to hear from you!


You can find me on

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