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

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 (most prominently, Engelmann, Vasishth, Engbert & Kliegl, ), 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.

The Effect of Prominence and Cue Association on Retrieval Processes: A Computational Account

“…This model is based on the general cognitive architecture ACT-R ("Adaptive Control of Thought-Rational," Anderson et al, 2004;Anderson & Lebiere, 1998). Over the last decade, the LV05 model has been widely used as a computational modeling framework by several research groups for investigating a range of empirical phenomena: (a) similaritybased interference effects (Dillon et al, 2013;J€ ager et al, 2015;Kush & Phillips, 2014;Nicenboim, Logacev, Gattei, & Vasishth, 2016;Nicenboim, Vasishth, Engelmann, & Suckow, 2018;Parker & Phillips, 2016Patil, Vasishth, & Lewis, 2016;Vasishth, Bruessow, Lewis, & Drenhaus, 2008); (b) the relative roles of predictive processing and memory effects (Boston, Hale, Vasishth, & Kliegl, 2011); (c) impairments in individuals with aphasia (M€ atzig, Vasishth, Engelmann, Caplan, & Burchert, 2018;Patil, Hanne, Burchert, De Bleser, & Vasishth, 2016); (d) the interaction between oculomotor control and sentence comprehension (Dotlacil, 2018;Engelmann, Vasishth, Engbert, & Kliegl, 2013); and (e) the effect of working memory capacity differences on underspecification ("good-enough" processing) in sentence comprehension (Engelmann, 2016). The model relies on the core assumptions of ACT-R that retrieving an item from memory is affected by activation decay and similarity-based interference.…”

Introduction to Volume 11, Issue 1 of topiCS

“…paper, “Creating Time: Social Collaboration in Music Improvisation,” has received its first citation. Likewise, impressive, three of the Best 2017 ICCM papers have each been cited once (Dotlacil, ; Maetzig, Vasishth, Engelmann, Caplan, & Burchert, ; van Vugt, van der Velde, & Investigators, ).…”

“…It is impressive that the von Zimmermann, Vicary, Sperling, Orgs, and Richardson paper, "The Choreography of Group Affiliation," has been cited twice; whereas the Walton et al paper, "Creating Time: Social Collaboration in Music Improvisation," has received its first citation. Likewise, impressive, three of the Best 2017 ICCM papers have each been cited once (Dotlacil, 2018;Maetzig, Vasishth, Engelmann, Caplan, & Burchert, 2018;van Vugt, van der Velde, & Investigators, 2018).…”

Editors’ Introduction: Cognitive Modeling at ICCM: Advancing the State of the Art

“…The paper by Dotlačil () moves the field forward by showing how ACT‐R models can be used to model eye movements during reading. The data fit were eye movements while participants were reading an Agatha Christie book, and the author shows how the model can predict the modulation of gaze duration and reading time on the basis of word frequency, word position, and word length.…”

“…Another important method in mathematical psychology is the systematic search of parameter spaces for the optimal parameters for predicting a certain behavior (while in the cognitive modeling community parameter searches are often done by hand). Such searches range from grid search (systematically trying out all possible combinations of model parameters) to stochastic methods such as genetic algorithms or Markov‐Chain Monte Carlo (see also Dotlačil, ). Moreover, such searches do not need to choose between the individual subject or group level but make more and more use of hierarchical Bayesian estimation methods (e.g., Lee, ), in which the parameter estimates of an individual participant are influenced by the rest of the group.…”