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An Expanded Set of Declarative Memory Functionalities in PyACTUp, a Python Implementation of ACT-UP’s Accountable Modeling

Ms. Yuxue Yang
University of Washington Seattle ~ Psychology
Don Morrison
Carnegie Mellon University, United States of America
Prof. Andrea Stocco
University of Washington ~ University of Washington
Mark Orr
University of Virginia ~ Biocomplexity Institute
Christian Lebiere
Department of Psychology, Carnegie Mellon University, Pittsburgh, PA 15213 USA

ACT-R, a well-established cognitive modeling architecture (Anderson, 2007) has been widely used in the field of cognitive psychology and neuroscience to interpret human cognition, memory formation and learning process. However, the programming difficulties in designing a model slows down the progress of cognitive modeling study. Inspired by Reitter and Lebiere (2010)’s ACT-UP, which is a subset of ACT-R declarative memory implementation, we introduce the Python implementation, PyACTUp, and expand its functionality to incorporate more important features from ACT-R. Current version of PyACT-UP provides great flexibility for modelers to define their own methods and meanwhile remains a simplified structure which is friendly to novice programmers.


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