| Paper: | PS-2A.72 | ||
| Session: | Poster Session 2A | ||
| Location: | H Lichthof | ||
| Session Time: | Sunday, September 15, 17:15 - 20:15 | ||
| Presentation Time: | Sunday, September 15, 17:15 - 20:15 | ||
| Presentation: | Poster | ||
| Publication: | 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany | ||
| Paper Title: | Bayesian parameter estimation for the SWIFT model of eye-movement control during reading | ||
| Manuscript: | Click here to view manuscript | ||
| License: |  This work is licensed under a Creative Commons Attribution 3.0 Unported License. |
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| DOI: | https://doi.org/10.32470/CCN.2019.1369-0 | ||
| Authors: | Stefan A. Seelig, Maximilian M. Rabe, Noa Malem-Shinitski, Sebastian Reich, Ralf Engbert, University of Potsdam, Germany | ||
| Abstract: | Dynamical models are increasingly contributing to the development of cognitive theory. Here we discuss an example for eye-movement control during reading. The SWIFT model (Engbert et al., 2005) is a stochastic dynamical system that predicts spatial fixation positions in a given text as well as fixation durations. We exploit the sequential nature of the likelihood for dynamical models. The likelihood function is a combination of spatial and temporal likelihood. While the spatial part is a pseudo-marginal likelihood, the temporal likelihood is obtained by numerical approximation. We use a fully Bayesian framework for parameter inference using an adaptive Markov Chain Monte Carlo (MCMC) procedure. As a result, we obtain model parameter estimates and credibility intervals on the level of individual readers. Interindividual parameter variations capture key features of the behavioral variability of eye movements observed in reading experiments. | ||