A joint analysis of dropout and learning functions in human decision-making with massive online data

The introduction of large-scale data sets in psychology allows for more robust accounts of various cognitive mechanisms, one of which is human learning. However, these data sets provide participants with complete autonomy over their own participation in the task, and therefore require precisely studying the factors influencing dropout alongside learning. In this work, we present such a data set where 1, 234, 844 participants play 10, 874, 547 games of a challenging variant of tic-tac-toe. We establish that there is a correlation between task performance and total experience, and independently analyze participants' dropout behavior and learning trajectories. We find evidence for stopping patterns as a function of playing strength and investigate the processes underlying playing strength increases with experience using a set of metrics derived from a planning model. Finally, we develop a joint model to account for both dropout and learning functions which replicates our empirical findings.