Bayesian parameter estimation for the SWIFT model of eye-movement control during reading
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Bayesian parameter estimation for the SWIFT model of eye-movement control during reading. / Seelig, Stefan A.; Rabe, Maximilian M.; Malem-Shinitski, Noa; Risse, Sarah; Reich, Sebastian; Engbert, Ralf.
In: Journal of Mathematical Psychology, Vol. 95, 102313, 04.2020.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Bayesian parameter estimation for the SWIFT model of eye-movement control during reading
AU - Seelig, Stefan A.
AU - Rabe, Maximilian M.
AU - Malem-Shinitski, Noa
AU - Risse, Sarah
AU - Reich, Sebastian
AU - Engbert, Ralf
N1 - Funding Information: This work was supported by grants from Deutsche Forschungsgemeinschaft, Germany ( SFB 1294 , project B03, project no. 318763901 to R.E. and S.Re.; SFB 1287 , project B03, project no. 317633480 to R.E.; grant RI 2504/1-1 to S.Ri.). We acknowledge a grant for computing time from Norddeutscher Verbund für Hoch- und Höchstleistungsrechnen, Germany (HLRN, grant bbx00001 ). Publisher Copyright: © 2020 Elsevier Inc.
PY - 2020/4
Y1 - 2020/4
N2 - Process-oriented theories of cognition must be evaluated against time-ordered observations. Here we present a representative example for data assimilation of the SWIFT model, a dynamical model of the control of fixation positions and fixation durations during natural reading of single sentences. First, we develop and test an approximate likelihood function of the model, which is a combination of a spatial, pseudo-marginal likelihood and a temporal likelihood obtained by probability density approximation Second, we implement a Bayesian approach to parameter inference using an adaptive Markov chain Monte Carlo procedure. Our results indicate that model parameters can be estimated reliably for individual subjects. We conclude that approximative Bayesian inference represents a considerable step forward for computational models of eye-movement control, where modeling of individual data on the basis of process-based dynamic models has not been possible so far.
AB - Process-oriented theories of cognition must be evaluated against time-ordered observations. Here we present a representative example for data assimilation of the SWIFT model, a dynamical model of the control of fixation positions and fixation durations during natural reading of single sentences. First, we develop and test an approximate likelihood function of the model, which is a combination of a spatial, pseudo-marginal likelihood and a temporal likelihood obtained by probability density approximation Second, we implement a Bayesian approach to parameter inference using an adaptive Markov chain Monte Carlo procedure. Our results indicate that model parameters can be estimated reliably for individual subjects. We conclude that approximative Bayesian inference represents a considerable step forward for computational models of eye-movement control, where modeling of individual data on the basis of process-based dynamic models has not been possible so far.
KW - Bayesian inference
KW - Dynamical models
KW - Eye movements
KW - Interindividual differences
KW - Likelihood function
KW - MCMC
KW - Reading
KW - Saccades
UR - http://www.scopus.com/inward/record.url?scp=85078134928&partnerID=8YFLogxK
U2 - 10.1016/j.jmp.2019.102313
DO - 10.1016/j.jmp.2019.102313
M3 - Journal article
AN - SCOPUS:85078134928
VL - 95
JO - Journal of Mathematical Psychology
JF - Journal of Mathematical Psychology
SN - 0022-2496
M1 - 102313
ER -
ID: 389895250