This page contains a list of (co-)authored publications and conference presentations. Please contact me if any of those are not accessible for you. To look at my CV, click here.
Preprints
- Rabe, M. M.
, Paape, D.
, Vasishth, S.
, & Engbert, R.
(2021). Dynamical cognitive modeling of syntactic processing and eye movement control in reading. Manuscript in preparation. PsyArxiv
Journal Articles
- Rabe, M. M.
, Chandra, J.
, Krügel, A.
, Seelig, S. A.
, Vasishth, S.
, & Engbert, R.
(in press). A Bayesian approach to dynamical modeling of eye-movement control in reading of normal, mirrored, and scrambled texts. Psychological Review. DOI PsyArxiv
- Engbert, R.
, Rabe, M. M.
, Kliegl, R.
, & Reich, S.
(2021). Sequential data assimilation of the SEIR model for COVID-19. Bulletin of Mathematical Biology, 83(1). DOI medRxiv PDF
- 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 PDF
- 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 arXiv PDF
- 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 PDF
Software
- Rabe, M. M.
, Schad, D. J.
, Vasishth, S.
, & Kliegl, R.
(2019). hypr: Hypothesis matrix translation [R package]. URL
- Rabe, M. M.
, Kliegl, R.
, & Schad, D. J.
(2019). designr: Balanced factorial designs with crossed random and fixed factors [R package]. URL
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. HDL PDF
- 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 PDF
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 PDF
- 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
- 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 PDF
- 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. PDF
- 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.