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Predicting human performance in interaction tasks allows designers ordevelopers to understand the expected performance of a target interface withoutactually testing it with real users. In this work, we present a deep neural netto model and predict human performance in performing a sequence of UI tasks. Inparticular, we focus on a dominant class of tasks, i.e., target selection froma vertical list or menu. We experimented with our deep neural net using apublic dataset collected from a desktop laboratory environment and a datasetcollected from hundreds of touchscreen smartphone users via crowdsourcing. Ourmodel significantly outperformed previous methods on these datasets.Importantly, our method, as a deep model, can easily incorporate additional UIattributes such as visual appearance and content semantics without changingmodel architectures. By understanding about how a deep learning model learnsfrom human behaviors, our approach can be seen as a vehicle to discover newpatterns about human behaviors to advance analytical modeling.

Predicting Human Performance in Vertical Menu Selection Using Deep Learning