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Recent publications

  1. Computational mechanisms underlying latent value updating of unchosen…

    Ben-Artzi, I., Kessler, Y., Nicenboim, B., & Shahar, N. (2023). Computational mechanisms underlying latent value updating of unchosen actions. Science Advances, 9(42), Article eadi2704. ,
  2. Bayes Factors for Mixed Models: a Discussion

    Doorn, J. V., Haaf, J. M., Stefan, A. M., Wagenmakers, E.-J., Cox, G. E., Davis-Stober, C. P., Heathcote, A., Heck, D. W., Kalish, M., Kellen, D., Matzke, D., Morey, R. D., Nicenboim, B., Ravenzwaaij, D. V., Rouder, J. N., Schad, D. J., Shiffrin, R. M., Singmann, H., Vasishth, S., ... Aust, F. (2023). Bayes Factors for Mixed Models: a Discussion. Computational Brain & Behavior, 6, 140-158.
  3. The CoFI Reader - A Continuous Flow of Information approach to modeli…

    Nicenboim, B. (2023). The CoFI Reader: A Continuous Flow of Information approach to modeling reading. In MathPsych/ICCM/EMPG 2023 https://mathpsych.org/presentation/998
  4. Sample size determination for bayesian hierarchical models commonly u…

    Vasishth, S., Yadav, H., Schad, D. J., & Nicenboim, B. (2023). Sample size determination for bayesian hierarchical models commonly used in psycholinguistics. Computational Brain & Behavior, 6, 102-126.
  5. Modeling sonority in terms of pitch intelligibility with the nucleus …

    Albert, A., & Nicenboim, B. (2022). Modeling sonority in terms of pitch intelligibility with the nucleus attraction principle. Cognitive Science, 46(7), 1-68. Article e13161.

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