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Part of the book series: Human–Computer Interaction Series ((HCIS))

Abstract

Predicting human performance in interaction tasks allows designers or developers to understand the expected performance of a target interface without actually testing it with real users. In this chapter, we are going to discuss how deep learning methods can be used to aid human performance prediction in the context of HCI. Particularly, we are going to look at three case studies. In the first case study, we discuss deep models for goal-driven human visual search on arbitrary web pages. In the second study, we show that deep learning models could successfully capture human learning effects from repetitive interaction with vertical menus. In the third case study, we describe how deep models can be combined with analytical understanding to capture high-level interaction strategies and low-level behaviors in touchscreen grid interfaces on mobile devices. In all these studies, we show that deep learning provides great capacity for modeling complex interaction behaviors, which would be extremely difficult for traditional heuristic-based models. Furthermore, we showcase different ways to analyze a learned deep model to obtain better model interpretability, and understanding of human behaviors to advance the science.

Arianna Yuan and Ken Pfeuffer conducted the work during an internship at Google Research.

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Yuan, A., Pfeuffer, K., Li, Y. (2021). Human Performance Modeling with Deep Learning. In: Li, Y., Hilliges, O. (eds) Artificial Intelligence for Human Computer Interaction: A Modern Approach. Human–Computer Interaction Series. Springer, Cham. https://doi.org/10.1007/978-3-030-82681-9_1

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  • DOI: https://doi.org/10.1007/978-3-030-82681-9_1

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