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Making Instance-based Learning Theory usable and understandable: The Instance-based Learning Tool

机译:使基于实例的学习理论变得可用和易于理解:基于实例的学习工具

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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.
机译:本文着重于创建和呈现用于开发人类行为的计算模型的用户友好体验。尽管人类行为的计算模型在认知心理学上享有丰富的历史,但它们缺乏广泛的影响,部分原因是除了建模过程的复杂性之外还需要技术知识和编程。我们描述了一种称为IBLTool的建模工具,该工具是基于实例的学习理论(IBLT)的计算实现。 IBLT是代表如何根据动态任务的经验来做出决策的理论。 IBLTool使IBLT对更广泛的认知和行为科学家社区变得可用和易于理解。该工具使用图形用户界面,逐步引导建模人员完成几个IBLT流程,并帮助建模人员得出特定任务中人类行为的预测。任务将与IBLTool连接并进行交互,并存储决策数据,而该工具则从执行该任务的模型中收集统计数据。我们解释了IBLTool的功能,并演示了针对Iowa Gambling任务设计和执行模型的具体示例。该示例旨在提供IBLTool功能的具体演示。

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