A commonly used approach to parameter estimation in computational models is the so-called grid search procedure: the entire parameter space is searched in small steps to determine the parameter value that provides the best fit to the observed data. This approach has several disadvantages: first, it can be computationally very expensive; second, one optimal point value of the parameter is reported as the best fit value; we cannot quantify our uncertainty about the parameter estimate. In the main journal article that this methods article accompanies (Jäger et al., 2020, Interference patterns in subject-verb agreement and reflexives revisited: A large-sample study, Journal of Memory and Language), we carried out parameter estimation using Approximate Bayesian Computation (ABC), which is a Bayesian approach that allows us to quantify our uncertainty about the parameter's values given data. This customization has the further advantage that it allows us to generate both prior and posterior predictive distributions of reading times from the cue-based retrieval model of Lewis and Vasishth, 2005.•Instead of the conventional method of using grid search, we use Approximate Bayesian Computation (ABC) for parameter estimation in the [4] model.•The ABC method of parameter estimation has the advantage that the uncertainty of the parameter can be quantified.
Keywords: Bayesian parameter estimation; Prior and posterior predictive distributions; Psycholinguistics.
© 2020 The Author(s). Published by Elsevier B.V.