This page contains a list of (co-)authored publications and conference presentations. Please contact me if any of those are not accessible for you.
Journal Articles
- Rabe, M. M., Vasishth, S., Hohenstein, S., Kliegl, R., & Schad, D. J. (2020). hypr: An R package for hypothesis-driven contrast coding. The Journal of Open Source Software, 5(48), 2134. doi: 10.21105/joss.02134.
- Seelig, S. A., Rabe, M. M., Malem-Shinitski, N., Risse, S., Reich, S., & Engbert, R. (2020). Bayesian parameter estimation for the SWIFT model of eye-movement control during reading. Journal of Mathematical Psychology, 95. doi: 10.1016/j.jmp.2019.102313. arXiv:1901.11110.
- Masson, M. E. J., Rabe, M. M., & Kliegl, R. (2017). Modulation of additive and interactive effects by trial history revisited. Memory & Cognition, 45(3), 480–492. doi: 10.3758/s13421-016-0666-z.
Preprints
- Engbert, R., Rabe, M. M., Kliegl, R., & Reich, S. (2020). Sequential data assimilation of the SEIR model for COVID-19. medRxiv. doi: 10.1101/2020.04.13.20063768.
- Rabe, M. M., Chandra, J., Krügel, A., Seelig, S. A., Vasishth, S., & Engbert, R. (2019). A Bayesian approach to dynamical modeling of eye-movement control in reading of normal, mirrored, and scrambled texts. Manuscript submitted for publication. doi: 10.31234/osf.io/nw2pb.
Theses
- Rabe, M. M. (2018). Generalized linear mixed modeling of Signal Detection Theory (Master of Science). Supervisors: D. S. Lindsay, M. E. J. Masson, and A. Krawitz. University of Victoria, BC, Canada.
- Rabe, M. M. (2015). Mixed model analysis of trial history in naming experiments (Bachelor of Science). Supervisors: R. Kliegl and M. E. J. Masson. Universität Potsdam, Germany. urn:nbn:de:kobv:517-opus4-82735.
Presentations
- Rabe, M. M. (November 2019). Eye-movement control during reading: Bayesian parameter inference of the dynamical SWIFT model. Invited talk presented at University of Victoria, BC, Canada.
- Rabe, M. M., Chandra, J., Krügel, A., Seelig, S. A., & Engbert, R. (November 2019). Bayesian inference of dynamical cognitive and oculomotor processes in the SWIFT model of reading. Poster presented at Psychonomic Society 60th Annual Meeting, Montréal, QC, Canada. doi: 10.17605/osf.io/mxq4n.
- Seelig, S. A., Rabe, M. M., Malem-Shinitski, N., Reich, S., & Engbert, R. (September 2019). Bayesian parameter estimation for the SWIFT model of eye-movement control during reading. Poster presented at Cognitive Computational Neuroscience (CCN) 2019, Berlin, Germany. doi: 10.32470/CCN.2019.1369-0.
- Rabe, M. M., Chandra, J., Krügel, A., Seelig, S. A., & Engbert, R. (August 2019). Bayesian inference of the SWIFT model: Reading mirrored, scrambled, and normal texts. Poster presented at European Conference on Eye Movements (ECEM) 2019, Alicante, Spain. doi: 10.17605/osf.io/s5jk2.
- Rabe, M. M., Seelig, S. A., Chandra, J., Vasishth, S., Reich, S., & Engbert, R. (March 2019). Parameter inference and model comparison in dynamical cognitive models: SWIFT. Poster presented at 2nd SFB 1294 Data Assimilation Spring School, Dierhagen, Germany.
- Rabe, M. M., Kliegl, R., & Lindsay, D. S. (November 2017). A Generalized linear mixed model approach to signal detection theory in recognition memory experiments. Poster presented at Psychonomic Society 58th Annual Meeting, Vancouver, BC, Canada.
- Rabe, M. M., Kliegl, R., & Lindsay, D. S. (May 2017). Generalized linear mixed model of signal detection theory. Poster presented at NOWCAM, Burnaby, BC, Canada.
- Fallow, K. M., Rabe, M. M., & Lindsay, D. S. (November 2015). Recognition memory response bias for paintings, words, and faces. Poster presented at Psychonomic Society 56th Annual Meeting, Chicago, IL, USA.
Software
- Rabe, M. M., Schad, D. J., Vasishth, S., & Kliegl, R. (2019). hypr: Hypothesis matrix translation [R package]. Retrieved from https://maxrabe.com/hypr.
- Rabe, M. M., Kliegl, R., & Schad, D. J. (2019). designr: Balanced factorial designs with crossed random and fixed factors [R package]. Retrieved from https://maxrabe.com/designr.