Validating and Refining Cognitive Process Models Using Probabilistic Graphical Models. (24th May 2022)

• Record Type: Journal Article
• Authors: Hiatt, Laura M.; Brooks, Connor; Trafton, J. Gregory
• Is Part Of: Topics in cognitive science. Volume 14:Number 4(2022)
• Page Start: 873
• Page End: 888
• Publication Date: 2022-05-24
• Subjects: ACT‐R -- Cognitive models -- Graphical models; Cognitive science -- Periodicals; Cognitive Science -- Periodicals; 153.05
• DOI: 10.1111/tops.12616
• Languages: English
• ISSNs: 1756-8757

Abstract

Abstract: We describe a new approach for developing and validating cognitive process models. In our methodology, graphical models (specifically, hidden Markov models) are developed both from human empirical data on a task and synthetic data traces generated by a cognitive process model of human behavior on the task. Differences between the two graphical models can then be used to drive model refinement. We show that iteratively using this methodology can unveil substantive and nuanced imperfections of cognitive process models that can then be addressed to increase their fidelity to empirical data. Abstract : We describe a new approach for developing and validating cognitive process models. We develop graphical models (specifically, hidden Markov models) both from human empirical data on a task, as well as from synthetic data traces generated by a cognitive process model of human behavior on the task. We show that considering differences between the two graphical models can unveil substantive and nuanced imperfections of cognitive process models that can then be addressed to increase their fidelity to empirical data.