Causal learning with two confounded causes over weeks

Abstract Prior research shows that, when making causal inferences, people can control for alternative causes. However, these studies utilize artificial inter-trial intervals on the order of seconds; in real-life situations, people often experience data over days and weeks (e.g., learning the effectiveness of two new medications over multiple weeks). Across two experiments, participants learned about two possible causes from data presented either in a more naturalistic paradigm (one trial per day for multiple weeks via smartphone) or in a traditional trial-by-trial paradigm (a rapid series of trials). The results show that people can control for alternative causes when learning over long timeframes, but they also exhibit non-normative discounting. The results also reveal that the extent to which people control and learn simple relations is suboptimal across both short and long timeframes.