  While the emerging research field of explainable artificial intelligence
(XAI) claims to address the lack of explainability in high-performance machine
learning models, in practice, XAI targets developers rather than actual
end-users. Unsurprisingly, end-users are often unwilling to use XAI-based
decision support systems. Similarly, there is limited interdisciplinary
research on end-users' behavior during XAI explanations usage, rendering it
unknown how explanations may impact cognitive load and further affect end-user
performance. Therefore, we conducted an empirical study with 271 prospective
physicians, measuring their cognitive load, task performance, and task time for
distinct implementation-independent XAI explanation types using a COVID-19 use
case. We found that these explanation types strongly influence end-users'
cognitive load, task performance, and task time. Further, we contextualized a
mental efficiency metric, ranking local XAI explanation types best, to provide
recommendations for future applications and implications for sociotechnical XAI
research.
