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Delays between causes and effects are commonly found in cause-effect relationships in real life. However, previous studies have only investigated delays on the order of seconds. In the current study we tested whether people can learn a cause- effect relation with hour long delays. The delays between the cause and effect were either 0, 3, 9, or 21 hours, and the study lasted 16 days. Surprisingly, we found that participants were able to learn the causal relation about equally as well in all four conditions. These findings demonstrate a remarkable ability to accurately learn causal relations in a realistic timeframe that has never been tested before. 

Interrupted time series analysis (ITSA) is a statistical procedure that evaluates whether an intervention causes a change in the intercept and/or slope of the time series. However, very little research has accessed causal learning in interrupted time series situations. We systematically investigated whether people are able to learn causal influences from a process akin to ITSA, and compared four different presentation formats of stimuli. We found that participants’ judgments agreed with ITSA in cases in which the pre-intervention slope is zero or in the same direction as the changes in intercept or slope. How- ever, participants had considerable difficulty controlling for pre-intervention slope when it is in the opposite direction of the changes in intercept or slope. The presentation formats didn’t affect judgments in most cases, but did in one. We discuss these results in terms of two potential heuristics that people might use aside from a process akin to ITSA. 

When making causal inferences, prior research shows that people are capable of controlling for alternative causes. These studies, however, 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). In the current study, participants learned about two possible causes from data presented in a traditional trial-by-trial paradigm (rapid series of trials) versus a more naturalistic paradigm (one trial per day for multiple weeks via smartphone). Our results suggest that while people are capable of detecting simple cause-effect relations that do not require controlling for another cause when learning over weeks, they have difficulty learning cause-effect relations that require controlling for alternative causes. 

Humans often rely on past experiences stored in long-term memory 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 successive series of learning trials. The rapid nature of this paradigm means that completing the task relies on working memory. In contrast, real-world events are typically spread out over longer periods of time, and therefore long-term memory must be used. We conducted a 24 day smartphone study to assess how well people can learn causal relationships in extended timeframes. Surprisingly, we found few differences in causal learning when subjects observed events in a traditional rapid series of 24 trials as opposed to one trial per day for 24 days. Specifically, subjects were able to detect causality for generative and preventive datasets and also exhibited illusory correlations in both the short-term and long-term designs. We discuss theoretical implications of this work. 

The current research investigates how people decide which of two options produces a better reward by repeatedly sampling from the options. In particular, it investigates the roles of two features of search, optional stopping and switch rate, on participants' final judgments of which option is better. First, in two studies, we found evidence for a new optional stopping effect; when participants stopped sampling right after experiencing a rare outcome, they made decisions as if they overweighted the rare outcome. Second, we investigated an effect proposed by Hills and Hertwig (2010) that people who frequently switch between options when sampling are more likely to make decisions consistent with underweighting rare outcomes. We conducted a theoretical analysis examining how switch rate can influence underweighting and how the type of decision problem moderates this effect. Informed by the theoretical analysis, we conducted four studies designed to test this effect with high power. None of the studies produced significant effects of switch rate. Lastly, the studies replicated a prior finding that optional stopping and switch rate are negatively correlated. In sum, this research elaborates a fuller understanding of the relation between search strategies (switch rate and optional stopping) on how people decide which option is better and their tendency to overweight versus underweight rare outcomes.