Dutt, Varun Gonzalez, Cleotilde Making Instance-based Learning Theory usable and understandable: The Instance-based Learning Tool <p>This paper focuses on the creation and presentation of a user-friendly experience for developing computational models of human behavior. Although computational models of human behavior have enjoyed a rich history in cognitive psychology, they have lacked widespread impact, partly due to the technical knowledge and programming required in addition to the complexities of the modeling process. We describe a modeling tool called IBLTool that is a computational implementation of the Instance-based Learning Theory (IBLT). IBLT is a theory that represents how decisions are made from experience in dynamic tasks. The IBLTool makes IBLT usable and understandable to a wider community of cognitive and behavioral scientists. The tool uses graphical user interfaces that take a modeler step-by-step through several IBLT processes and help the modeler derive predictions of human behavior in a particular task. A task would connect and interact with the IBLTool and store the decision-making data while the tool collects statistical data from the execution of a model for the task. We explain the functioning of the IBLTool and demonstrate a concrete example of the design and execution of a model for the Iowa Gambling task. The example is intended to provide a concrete demonstration of the capabilities of the IBLTool.</p> Instance-based Learning Theory (IBLT);Instance-based Learning Tool (IBLTool);Cognitive modeling;Decisions from experience;Iowa Gambling task (IGT);ACT-R 2012-07-01
    https://kilthub.cmu.edu/articles/journal_contribution/Making_Instance-based_Learning_Theory_usable_and_understandable_The_Instance-based_Learning_Tool/6571250
10.1184/R1/6571250.v1