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

Topics in Cognitive Science 10 (1):144-160 (2018)

Abstract
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, the model achieves the fit by estimating free parameters of ACT-R using Bayesian estimation and Markov-Chain Monte Carlo 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.
Keywords ACT‐R  Bayesian inference of ACT‐R parameters  Eye tracking  Modeling eye tracking  Modeling eye‐tracking corpus data  Parsing
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DOI 10.1111/tops.12315
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Acquisition of Cognitive Skill.John R. Anderson - 1982 - Psychological Review 89 (4):369-406.

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