Cognitive Models for Human Error Generation and Detection

Human errors can have significant consequences in various domains, such as healthcare, transportation, and finance. The ability to identify and mitigate human errors is critical for ensuring the safety and well-being of individuals and organizations. Cognitive models have the potential to inform the designers of systems about the potential for errors. Replacing users with models that simulate users’ behaviors has been a long-standing vision in interface design. Cognitive models can simulate users' cognitive processes and behaviors, but they cannot fully interact with user interfaces and simulate all types of behaviors. These models currently do not have the ability to detect and mitigate human errors, and there is still much progress to be made in incorporating error handling. This work proposed two possible approaches to address the generation and detection of human errors in cognitive models: Deterministic Error Cognitive Model and Automatic Error Cognitive Model. We model user errors in Microsoft Excel. Excel is a widely used software application that plays an important role in various industries, such as data analysis. Users make errors while working with Excel spreadsheets, which can lead to financial losses, errors in data analysis, and other negative outcomes. We utilized existing data from a behavioral study that involved 23 participants performing a spreadsheet task in Excel. We focus on the errors that arise from the participants typing. Understanding the cognitive processes that contribute to user typing errors in Excel can help improve the software's design, training programs for users, and error detection methods. Studying user errors in a task can also identify common mistakes made by users while completing the task and develop strategies to prevent or mitigate them. Additionally, understanding sub-tasks with higher user errors can help us design better task instructions that can reduce the cognitive load on users and reduce the likelihood of errors. The performance of models is compared with human data, and while both models show a high correlation, the Automatic Error Cognitive Model predicts user behavior with a lower error rate. Furthermore, both models operate on the same interface that users interact with, utilizing a vision and motor extension tool called VisiTor (Vision + Motor).