Humans often rely on past experiences stored in long-termmemory to predict the outcome of an event. In traditional lab-based experiments (e.g., causal learning, probability learning,etc.), these observations are compressed into a successiveseries of learning trials. The rapid nature of this paradigmmeans that completing the task relies on working memory. Incontrast, real-world events are typically spread out over longerperiods of time, and therefore long-term memory must be used.We conducted a 24 day smartphone study to assess how wellpeople can learn causal relationships in extended timeframes.Surprisingly, we found few differences in causal learning whensubjects observed events in a traditional rapid series of 24 trialsas opposed to one trial per day for 24 days. Specifically,subjects were able to detect causality for generative andpreventive datasets and also exhibited illusory correlations inboth the short-term and long-term designs. We discusstheoretical implications of this work.

When making causal inferences, prior research shows that peopleare capable of controlling for alternative causes. These studies,however, utilize artificial inter-trial intervals on the order ofseconds; in real-life situations people often experience data overdays and weeks (e.g., learning the effectiveness of two newmedications over multiple weeks). In the current study, participantslearned about two possible causes from data presented in atraditional trial-by-trial paradigm (rapid series of trials) versus amore naturalistic paradigm (one trial per day for multiple weeks viasmartphone). Our results suggest that while people are capable ofdetecting simple cause-effect relations that do not requirecontrolling for another cause when learning over weeks, they havedifficulty learning cause-effect relations that require controlling foralternative causes.