In algorithm augmented decision-making, humans must successfully judge when to follow or reject algorithmic advice. Here, research showed that humans tend to reject algorithmic advice after experiencing algorithmic errors. This more severe response to incorrect algorithmic advice compared to incorrect human advice gave rise to the definition of, and research on, the phenomenon of algorithm aversion. However, empirical findings on algorithm aversion are conflicting and mostly focused on the decision itself while neglecting the cognitive processes from receiving incorrect advice to deciding. Using a multi-trial mouse tracking experiment, we aim to better understand the emergence of algorithm aversion by investigating decisional conflicts reflected in cognitive process data. Through our research, we mainly aim to contribute to research on algorithm aversion and the IS community’s methodological toolkit while our insights on decisional conflicts can further inform practitioners on how to responsibly enable and onboard users of algorithms.