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Building an ACT‐R Reader for Eye‐Tracking Corpus Data

Building an ACT‐R Reader for Eye‐Tracking Corpus Data

Published in: Topics in Cognitive Science, Volume 10, Issue 1, 144-160

“Cognitive architectures have often been applied to data from individual experiments. In this paper, I develop an ACT‐R reader that can model a much larger set of data, eye‐tracking corpus data. It is shown that the resulting model has a good fit to the data for the considered low‐level processes. Unlike previous related works (most prominently, Engelmann, Vasishth, Engbert & Kliegl, 2013 ), the model achieves the fit by estimating free parameters of ACT‐R using Bayesian estimation and Markov‐Chain Monte Carlo (MCMC) techniques, rather than by relying on the mix of manual selection + default values. The method used in the paper is generalizable beyond this particular model and data set and could be used on other ACT‐R models.”

Written by: Jakub Dotlacil
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