Cognitive modeling tools have been widely used by researchers and practitioners to help design, evaluate, and study computer user interfaces (UIs). Despite their usefulness, large-scale modeling tasks can still be very challenging due to the amount of manual work needed. To address this scalability challenge, we propose CogTool+, a new cognitive modeling software framework developed on top of the well-known software tool CogTool. CogTool+ addresses the scalability problem by supporting the following key features: (1) a higher level of parameterization and automation; (2) algorithmic components; (3) interfaces for using external data; and (4) a clear separation of tasks, which allows programmers and psychologists to define reusable components (e.g., algorithmic modules and behavioral templates) that can be used by UI/UX researchers and designers without the need to understand the low-level implementation details of such components. CogTool+ also supports mixed cognitive models required for many large-scale modeling tasks and provides an offline analyzer of simulation results. In order to show how CogTool+ can reduce the human effort required for large-scale modeling, we illustrate how it works using a pedagogical example, and demonstrate its actual performance by applying it to large-scale modeling tasks of two real-world user-authentication systems.

中文翻译：

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齿轮工具+

认知建模工具已被研究人员和从业者广泛用于帮助设计、评估和研究计算机用户界面 (UI)。尽管它们很有用，但由于需要大量的手动工作，大规模建模任务仍然非常具有挑战性。为了应对这一可扩展性挑战，我们提出了 CogTool+，这是一种在知名软件工具 CogTool 之上开发的新认知建模软件框架。CogTool+ 通过支持以下关键特性解决了可扩展性问题： (1) 更高水平的参数化和自动化；(2) 算法组件；(3) 使用外部数据的接口；(4) 明确的任务分离，允许程序员和心理学家定义可重用的组件（例如，算法模块和行为模板）可供 UI/UX 研究人员和设计人员使用，而无需了解此类组件的低级实现细节。CogTool+ 还支持许多大型建模任务所需的混合认知模型，并提供模拟结果的离线分析器。为了展示 CogTool+ 如何减少大规模建模所需的人力，我们使用一个教学示例来说明它的工作原理，并通过将其应用于两个真实世界用户身份验证的大规模建模任务来展示其实际性能系统。