Close
This site uses cookies

By using this site, you consent to our use of cookies. You can view our terms and conditions for more information.

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

Authors
Cher 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
Florida Institute for Human & Machine Cognition ~ Dept. of Intelligent Systems and Robotics
Christian Lebiere
Department of Psychology, Carnegie Mellon University, Pittsburgh, PA 15213 USA
Abstract

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.

Discussion
New

There is nothing here yet. Be the first to create a thread.

Cite this as:

Yang, Y., Morrison, D., Stocco, A., Orr, M., & Lebiere, C. (2020, July). An Expanded Set of Declarative Memory Functionalities in PyACTUp, a Python Implementation of ACT-UP’s Accountable Modeling. Paper presented at Virtual MathPsych/ICCM 2020. Via mathpsych.org/presentation/201.