Udo Böhm

I am postdoc in the Machine Learning Group at CWI (national research institute for mathematics and computer science) and a teacher at the Psychological Methods Unit at the University of Amsterdam. I received my PhD in Mathematical Psychology in 2018 from the University of Groningen. My work focusses on the development of computational and statistical (mostly Bayesian) methods for evidence accumulation models.

During my PhD I investigated how environmental factors such as time pressure and reward structures should be accommodated in diffusion models of perceptual decision-making. In this context I also developed hierarchical Bayesian methods for parameter estimation and model comparison. My postdoctoral research aims to extend this statistical framework to ballistic evidence accumulation models, and to develop off-the-shelf solutions for applying evidence accumulation models to ANOVA and regression-type experimental designs.

My CV

Publications by Udo Böhm

Boehm, U., Cox, S., Gantner, G. & Stevenson, R. (submitted). Efficient Numerical Approximation of
a Non Regular Fokker–Planck Equation Associated With First-Passage Time Distributions. ArXiv.
*Boehm, U., Cox, S., Gantner, G. & Stevenson, R. (in press). Fast solutions for the first-passage distribution
of diffusion models with space-time-dependent drift functions and time-dependent boundaries.
Journal of Mathematical Psychology, 105, 102613. (*authors listed alphabetically) [OSF repository]
Heck, D. W., Boehm, U., Boeing-Messing, F., Buerkner, P.-C., Derks, K.,…, & Hoijtink, H. (in press).
A review of applications of the Bayes factor in psychological research. Psychological Methods.
Boehm, U., Marsman, M., van der Maas, H. L. J., Maris, G. (in press). An attention-based diffusion
model for psychometric analyses. Psychometrika.
Retzler, C., Boehm, U., Jing, C., Cochrane, A., & Manning, C. (2021). Prior information use and
response caution in perceptual decision-making: No evidence for a relationship with autistic-like traits.
Quarterly Journal of Experimental Psychology, 74(11): 1953–1965.
Manning, C., Wagenmakers, E.-J., Norcia, A. M., Serif, G., & Boehm, U. (2021). Perceptual decisionmaking
in children: Age-related differences and EEG correlates. Computational Brain & Behavior, 4, 53-69.
Boehm, U., Evans, N.J., Gronau, Q.F, Matzke, D., Wagenmakers, E.-J., Heathcote, A. (2023). Inclusion Bayes Factors for mixed hierarchical Diffusion Decision Models. Psychological Methods.
Aczel, B., Szaszi, B., Nilsonne, G., van den Akker, O.R., Albers, C.J., van Assen, M.A.L.M., Bastiaansen, J. A., Benjamin, D., Boehm, U., …, Matzke, D., …, & Wagenmakers, E.-J. (2021). Consensus-based guidance for conducting and reporting multi-analyst studies. eLife,10:e7218.
Boehm, U., Matzke, D., Gretton, M., Castro, S., Cooper, J., Skinner, M., Strayer, D., & Heathcote, A. (2021). Real-time prediction of fluctuations in cognitive workload. Cognitive Research: Principles and Implications, 6:30.
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 & Review, 28, 813–826.
Boehm, U., Van Maanen, L., Evans, N., Brown, S., & Wagenmakers, E.-J. (2020). A theoretical analysis of the reward rate optimality of collapsing decision criteria. Attention, Perception & Psychophysics, 82 (3),
1520-1534.
Boehm, U., Steingroever, H., & Wagenmakers, E-J. (2018). Using Bayesian regression to test hypotheses
about relationships between parameters and covariates in cognitive models. Behavior Research Methods,
50 (3), 1248–1269.
Boehm, U., Annis, J., Frank, M.J., Hawkins, G.E., Heathcote, A., Kellen, D., …, Matzke, D., & Wagenmakers, E.-J. (2018). Estimating between-trial variability parameters of the diffusion decision model: Expert advice and recommendations. Journal of Mathematical Psychology, 87, 46-75. [Supplementary Materials & Data]
Boehm, U., Marsman, M., Matzke, D., & Wagenmakers, E.-J. (2018). On the importance of avoiding shortcuts in applying cognitive models to hierarchical data. Behavioral Research Methods, 50, 1614-1631.
Matzke, D., Boehm, U., & Vandekerckhove, J. (2018). Bayesian inference for psychology. Part III: Parameter estimation in nonstandard models. Psychonomic Bulletin & Review, 25, 77-101.
Ly, A., Boehm, U., Heathcote, A., Turner, B.M., Forstmann, B., Marsman, M., & Matzke, D. (2018). A flexible and efficient hierarchical Bayesian approach to the exploration of individual differences in cognitive-model-based neuroscience. In A.A. Moustafa (Ed.), Computational models of brain and behavior (pp. 467-480). Wiley Blackwell.
Evans, N. J., Hawkins, G. E., Boehm, U., Wagenmakers, E.-J. & Brown, S. D. (2017). The computations
that support simple decision-making: A comparison between the diffusion and urgency-gating models. Scientific Reports, 7 (1), 16433.
Gronau, Q. F., Sarafoglou, A., Matzke, D., Ly, A., Boehm, U., Marsman, M., et al. (2017). A tutorial on bridge sampling. Journal of Mathematical Psychology, 81, 80-97.
Boehm, U., Hawkins, G.E., Brown, S., Van Rijn, H., & Wagenmakers, E.-J. (2016). Of monkeys and
men: Impatience in perceptual decision-making. Psychonomic Bulletin & Review, 23 (3), 738-749.
Boehm, U., Van Maanen, L., Forstmann, B., & Van Rijn, H. (2014). Trial-by-trial fluctuations in CNV
amplitude reflect anticipatory adjustment of response caution. NeuroImage, 96, 95-105.