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Process-oriented theories of cognition must be evaluated against time-orderedobservations. Here we present a representative example for data assimilation ofthe SWIFT model, a dynamical model of the control of spatial fixation positionand fixation duration during reading. First, we develop and test an approximatelikelihood function of the model, which is a combination of a pseudo-marginalspatial likelihood and an approximate temporal likelihood function. Second, weuse a Bayesian approach to parameter inference using an adapative Markov chainMonte Carlo procedure. Our results indicate that model parameters can beestimated reliably for individual subjects. We conclude that approximativeBayesian inference represents a considerable step forward for the area ofeye-movement modeling, where modelling of individual data on the basis ofprocess-based dynamic models has not been possible before.

Bayesian parameter estimation for the SWIFT model of eye-movement control during reading