Making Instance-based Learning Theory Usable,Transparent,and Understandable

Abstract

Although cognitive models of human behavior enjoy a rich history in cognitive psychology, they lack a widespread impact, partly due to the complexities of the modeling process including the need to know software programming. We will demonstrate a modeling tool, called IBLTool, which represents Instance-based Learning Theory (IBLT). IBLT is a theory of decisions from experience in interactive, dynamic environments. IBLTool addresses the complexity and programming challenges in cognitive modeling and helps in the development of cognitive models for a particular task. The IBLTool makes IBLT usable, transparent, and understandable for the cognitive modeling community. The tool uses graphical user interfaces to represent the modeling process and knowledge representations from the IBLT in a step-by-step manner. To derive predictions of human behavior in the IBLTool for a decision task, a task implementation, independent from the IBLTool connects and interacts with the tool interactively as a human would. Data about human behavior is collected as part of the task, while the IBLTool collects data about the execution of the model and its parameters. We will introduce IBLT and provide a step-by-step demonstration of building cognitive models with the IBLTool.

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