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

## TL;DR

This paper introduces a Bayesian parameter estimation method for the SWIFT eye-movement model during reading, enabling reliable individual data fitting and advancing process-based dynamic modeling in eye-tracking research.

## Contribution

It develops an approximate likelihood function and applies Bayesian inference with MCMC, allowing for individual-level parameter estimation in the SWIFT model.

## Key findings

- Parameters can be reliably estimated for individual subjects.
- Approximate Bayesian inference advances eye-movement modeling.
- Method enables process-based dynamic model fitting to individual data.

## Abstract

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 spatial fixation position and fixation duration during reading. First, we develop and test an approximate likelihood function of the model, which is a combination of a pseudo-marginal spatial likelihood and an approximate temporal likelihood function. Second, we use a Bayesian approach to parameter inference using an adapative 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 the area of eye-movement modeling, where modelling of individual data on the basis of process-based dynamic models has not been possible before.