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Results: 24
Number of items: 24
  • Open Access
    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. https://doi.org/10.3758/s13423-020-01798-5
  • Manning, C., Wagenmakers, E.-J., Norcia, A. M., Scerif, G., & Boehm, U. (2020). EEG data supporting the published article: Perceptual decision-making in children: Age-related differences and EEG correlates. [Data set]. Figshare. https://doi.org/10.6084/m9.figshare.12378281
  • Manning, C., Wagenmakers, E.-J., Norcia, A. M., Scerif, G., & Boehm, U. (2020). Modelling files supporting the published article: Perceptual decision-making in children: Age-related differences and EEG correlates [Data set]. Figshare. https://doi.org/10.6084/m9.figshare.11931714
  • Open Access
    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
  • Open Access
    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. https://doi.org/10.31234/osf.io/dhb7x, https://doi.org/10.1007/s42113-019-00070-x
  • 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
  • Open Access
    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. https://doi.org/10.3758/s13423-017-1394-5
  • Open Access
    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
  • Open Access
    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
  • Open Access
    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
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