U. (Udo) Böhm PhD
Post-doctoral researcher
Faculty of Social and Behavioural Sciences
Programme group Psychological Methods
Visiting address
- Nieuwe Achtergracht 129
Postal address
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Postbus 15906
1001 NK Amsterdam
Contact details
Social media
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Profile
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.
Expertise and research fields
- Mathematical Psychology
- Bayesian Statistics
Relevant links
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Teaching
- Basic skills in programming, statistics and mathematics
- Calculus
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Research
Current research projects
- Efficient computation of first-passage distributions
- Bayesian estimation of evidence-accumulation models
Research grants & honours
- NWO Veni grant (VI.Veni.201G.045)
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Publications
2023
- Boehm, U., Evans, N. J., Gronau, Q. F., Matzke, D., Wagenmakers, E. J., & Heathcote, A. J. (2023). Inclusion Bayes Factors for Mixed Hierarchical Diffusion Decision Models. Psychological Methods. Advance online publication. https://doi.org/10.31234/osf.io/45t2w, https://doi.org/10.1037/met0000582
2022
Boehm, U., Cox, S., Gantner, G., & Stevenson, R. (2022). Efficient numerical approximation of a non-regular Fokker–Planck equation associated with first-passage time distributions. Bit : numerical mathematics , 62(4), 1355–1382 . Advance online publication. https://doi.org/10.1007/s10543-022-00914-2 [details]
2021
- Aczel, B., Szaszi, B., Nilsonne, G., van den Akker, O. R., Albers, C. J., van Assen, M. A., Bastiaansen, J. A., Benjamin, D., Boehm, U., Botvinik-Nezer, R., Bringmann, L. F., Busch, N. A., Caruyer, E., Cataldo, A. M., Cowan, N., Delios, A., van Dongen, N. N. N., Donkin, C., van Doorn, J. B., ... Wagenmakers, E.-J. (2021). Science Forum: Consensus-based guidance for conducting and reporting multi-analyst studies. eLife, 10, Article e72185. https://doi.org/10.7554/eLife.72185 [details]
- Boehm, U., Cox, S., Gantner, G., & Stevenson, R. (2021). 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, Article 102613. https://doi.org/10.1016/j.jmp.2021.102613 [details]
- Boehm, U., Marsman, M., van der Maas, H. L. J., & Maris, G. (2021). An Attention-Based Diffusion Model for Psychometric Analyses. Psychometrika, 86(4), 938-972. https://doi.org/10.1007/s11336-021-09783-0 [details]
- 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, Article 30. https://doi.org/10.1186/s41235-021-00289-y [details]
- Manning, C., Wagenmakers, E-J., Norcia, A. M., Scerif, G., & Böhm, U. (2021). Perceptual Decision-Making in Children: Age-Related Differences and EEG Correlates. Computational Brain & Behavior, 4(1), 53-69. Advance online publication. https://doi.org/10.31234/osf.io/jwsmb, https://doi.org/10.1007/s42113-020-00087-7 [details]
- Retzler, C., Boehm, U., Cai, J., 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. https://doi.org/10.1177/17470218211019939 [details]
- van Doorn, J., van den Bergh, D., Böhm, U., Dablander, F., Derks, K., Draws, T., Etz, A., Evans, N. J., Gronau, Q. F., Haaf, J. M., Hinne, M., Kucharský, Š., Ly, A., Marsman, M., Matzke, D., Komarlu Narendra Gupta, A. R., Sarafoglou, A., Stefan, A., Voelkel, J. G., & Wagenmakers, E-J. (2021). The JASP guidelines for conducting and reporting a Bayesian analysis. Psychonomic Bulletin & Review, 28(3), 813–826. Advance online publication. https://doi.org/10.3758/s13423-020-01798-5 [details]
2020
- Boehm, U., van Maanen, L., Evans, N. J., Brown, S. D., & Wagenmakers, E.-J. (2020). A theoretical analysis of the reward rate optimality of collapsing decision criteria. Attention, Perception, and Psychophysics, 82(3), 1520-1534. https://doi.org/10.3758/s13414-019-01806-4 [details]
- Ly, A., Stefan, A., van Doorn, J., Dablander, F., van den Bergh, D., Sarafoglou, A., Kucharský, S., Derks, K., Gronau, Q. F., Raj, A., Boehm, U., van Kesteren, E-J., Hinne, M., Matzke, D., Marsman, M., & Wagenmakers, E-J. (2020). The Bayesian Methodology of Sir Harold Jeffreys as a Practical Alternative to the P Value Hypothesis Test. Computational Brain & Behavior, 3(2), 153-161. Advance online publication. https://doi.org/10.31234/osf.io/dhb7x, https://doi.org/10.1007/s42113-019-00070-x [details]
2018
- Boehm, U., Annis, J., Frank, M. J., Hawkins, G. E., Heathcote, A., Kellen, D., Krypotos, A-M., Lerche, V., Logan, G. D., Palmeri, T. J., van Ravenzwaaij, D., Servant, M., Singmann, H., Starns, J. J., Voss, A., Wiecki, T. V., Matzke, D., & Wagenmakers, E-J. (2018). Estimating across-trial variability parameters of the Diffusion Decision Model: Expert advice and recommendations. Journal of Mathematical Psychology, 87, 46-75. https://doi.org/10.1016/j.jmp.2018.09.004 [details]
- Boehm, U., Marsman, M., Matzke, D., & Wagenmakers, E-J. (2018). On the importance of avoiding shortcuts in applying cognitive models to hierarchical data. Behavior Research Methods, 50(4), 1614-1631. https://doi.org/10.3758/s13428-018-1054-3 [details]
- 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. https://doi.org/10.3758/s13428-017-0940-4 [details]
- Matzke, D., Boehm, U., & Vanderkerckhove, J. (2018). Bayesian inference for psychology. Part III: Parameter estimation in nonstandard models. Psychonomic Bulletin & Review, 25(1), 77-101. Advance online publication. https://doi.org/10.3758/s13423-017-1394-5 [details]
2017
- 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, Article 16433. https://doi.org/10.1038/s41598-017-16694-7 [details]
- Gronau, Q. F., Sarafoglou, A., Matzke, D., Ly, A., Boehm, U., Marsman, M., Leslie, D. S., Forster, J. J., Wagenmakers, E-M., & Steingroever, H. (2017). A tutorial on bridge sampling. Journal of Mathematical Psychology, 81, 80-97. Advance online publication. https://doi.org/10.1016/j.jmp.2017.09.005 [details]
2016
- 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. Advance online publication. https://doi.org/10.3758/s13423-015-0958-5 [details]
2014
- 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. Advance online publication. https://doi.org/10.1016/j.neuroimage.2014.03.063 [details]
2021
- Boehm, U., Matzke, D., Gretton, M., Castro, S., Cooper, J., Skinner, M., Strayer, D., & Heathcote, A. (2021). Correction to: Real-time prediction of short-timescale fluctuations in cognitive workload. Cognitive Research: Principles and Implications, 6, Article 62. https://doi.org/10.1186/s41235-021-00328-8
2020
- Heck, D. W., Böhm, U., Böing-Messing, F., Bürkner, P-C., Derks, K., Dienes, Z., Fu, Q., Gu, X., Karimova, D., Kiers, H. A. L., Klugkist, I., Kuiper, R., Lee, M. D., Leenders, R., Leplaa, H. J., Linde, M., Ly, A., Meijerink-Bosman, M., Moerbeek, M., ... Hoijtink, H. (2020). A review of applications of the Bayes factor in psychological research. (PsyArXiv). https://doi.org/10.31234/osf.io/cu43g
2018
- Ly, A., Böhm, 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. https://doi.org/10.1002/9781119159193.ch34 [details]
2020
- Manning, C., Norcia, A. M., Scerif, G., Boehm, U. & Wagenmakers, E.-J. (2020). Modelling files supporting the published article: Perceptual decision-making in children: Age-related differences and EEG correlates. Figshare. https://doi.org/10.6084/m9.figshare.11931714
- Manning, C., Norcia, A. M., Scerif, G., Boehm, U. & Wagenmakers, E.-J. (2020). EEG data supporting the published article: Perceptual decision-making in children: Age-related differences and EEG correlates.. Figshare. https://doi.org/10.6084/m9.figshare.12378281
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Ancillary activities
No ancillary activities
Boehm, U., Cox, S., Gantner, G., & Stevenson, R. (2022). Efficient numerical approximation of a non-regular Fokker–Planck equation associated with first-passage time distributions. Bit : numerical mathematics , 62(4), 1355–1382 . Advance online publication.