A key advantage of conceptual models is that their quality can be evaluated and validated before beginning the costlier stages of information system development. Few research studies investigate the validation process for such models, particularly regarding multiplicities, even though multiplicity mistakes can be very costly. We investigated the validation of conceptual model multiplicities, varying how closely natural language statements of business rules match the models that purport to represent those rules. Participants in an eye tracking experiment completed validation tasks in which they viewed a statement and an accompanying UML class diagram in which a specified multiplicity was consistent with the statement (valid) or inconsistent with the statement (invalid). We varied whether the focal multiplicity was a minimum or a maximum and varied the class diagram’s semantics and order compared to that of the statement. Logistic regression was used to analyze the relationship between accuracy and the experimental manipulations and controls. The results show that the odds of accuracy in validating class diagrams that used synonyms instead of the exact statement terminology were only 0.46 times the odds of accuracy when the class diagram and statement words matched, showing a costly effect of synonymy. Interestingly, independent of the three levels of relative semantics, the odds of accuracy were 0.48 times when class diagrams were consistent with business rules as they were when class diagrams were inconsistent with business rules. To gain insight into cognition under correct task performance, we conducted additional linear regression analysis on various eye tracking metrics for only the accurate responses. Again, synonymy was observed to be costly, with a cognitive burden of increased integrative transitions between statement and model in the range of 39 to 66%.

概念模型的一个关键优势是可以在开始信息系统开发成本更高的阶段之前评估和验证它们的质量。很少有研究调查此类模型的验证过程，特别是关于多重性，即使多重性错误可能会付出非常高昂的代价。我们调查了概念模型多重性的验证，改变了业务规则的自然语言语句与声称代表这些规则的模型的匹配程度。眼动追踪实验的参与者完成了验证任务，他们查看了一个陈述和一个随附的 UML 类图，其中指定的多重性与陈述一致（有效）或与陈述不一致（无效）。我们改变了焦点多重性是最小值还是最大值，并与语句相比改变了类图的语义和顺序。逻辑回归用于分析准确性与实验操作和控制之间的关系。结果表明，验证使用同义词而不是确切的陈述术语的类图的准确率仅为类图和陈述词匹配时的准确率的 0.46 倍，显示出同义词的代价高昂的影响。有趣的是，独立于三个级别的相对语义，当类图与业务规则一致时，准确率是类图与业务规则不一致时的 0.48 倍。在正确的任务表现下深入了解认知，我们对各种眼动追踪指标进行了额外的线性回归分析，仅针对准确的响应。同样，同义词被观察到代价高昂，陈述和模型之间的综合转换增加的认知负担在 39% 到 66% 之间。