The tendency to overestimate one’s knowledge has been shown in many domains including the innerworkings of everyday objects. This Illusion of Explanatory Depth (IOED) can be broken through the act of generating a causal explanation, although the reason as to why has yet to be explored. In this study, we investigate what characteristics of a generated explanation result in people recognizing their perceived lack of knowledge. Participants completed a typical IOED paradigm for devices, followed by rating their perceived completeness and accuracy for the explanations they generated. We also coded the explanations to determine their causal complexity. We found that lower ratings of overall perceived completeness and a sense of incomplete big explanatory components were predictive of a larger decrease in perceived understanding for that device post-explanation. Fewer causal links within an explanation also predicted a larger decrease in understanding ratings, suggesting that producing an explanation with a lower causal complexity led to a decrease in perceived understanding of that device. We discuss the implications of these results in relation to explanation characteristics that may cause a person’s illusion of understanding to break and proposed origins of the IOED phenomenon.

When someone realizes they do not actually know how a can opener works, do they think it is just a one-time bout of overconfidence? Or, do they assume they lack understanding of all the devices in their home? Causal knowledge is a fundamental part of both daily functioning and long-term learning. Previous studies have shown that writing out a causal explanation has the ability to induce knowledge reassessment and decrease inflated perceptions of knowledge specific to the concept being explained. However, the generalization of this knowledge reassessment has only recently been explored. In this preregistered experiment, we used the Illusion of Explanatory Depth (IOED) paradigm to see whether a decrease in perceived understanding of an explained item affects the perceived understanding of an item that was not asked to be explained. We also assessed the effect of explanation quality on this transfer of knowledge. Results showed that knowledge reassessment for explained items led to an even greater reassessment for unexplained items, suggesting possible overgeneralization. While explanation quality influenced knowledge reassessment for explained items, it did not for unexplained items. We discuss the possible reasons for these results as well as future studies to help understand the boundaries of knowledge reassessment.

We are often confronted with new causal information aboutthe world, such as what causes a disease. What we think weknow may influence if and how we choose to use this new in-formation. Yet as prior work has shown, we are not alwayssuccessful at evaluating our own knowledge. We explored howhelping people better understand what they know about a do-main can influence their ability to use new causal informationin a decision-making context. Participants self-assessed theirknowledge (Experiment 1) or completed an objective assess-ment of their knowledge (Experiment 2) of diabetes, beforemaking diabetes-related decisions, either with or without newcausal information. Without a knowledge assessment, partic-ipants were less accurate with new causal information com-pared to without such information, replicating previous work.However, reassessing their knowledge increased participants’decision-making accuracy with causal information. We dis-cuss why helping people realize the limits of their causal un-derstanding may make them better supplement it with new in-formation.

While explanations provide the power to understand the worldaround us, people are often overconfident about their ownunderstanding. We explored how people’s perceptions of theirunderstanding of phenomena is related to endorsement ofconspiracy theories. We first tested people’s perceptions oftheir understanding of the 2016 Presidential electoral processand then measured their beliefs that the election itself wasillegitimate, a form of conspiratorial belief. We found thatparticipants who still endorsed high levels of understandingafter generating an explanation for the 2016 election were alsomore likely to endorse the election was illegitimate. However,this finding only obtained for participants who voted for thelosing candidate. These results suggest interesting avenues forexploring individual differences that may be related to theillusion of explanatory depth.

People bring many beliefs to everyday decisions. Beliefs, such as those about health, can vary in both degree to which people believe them and degree to which they are correct. While prior work has found that it is difficult to correct mistaken beliefs, it has also shown that causal models may be more effective than other types of information for getting people to adopt correct information. However, it is an open question as to whether such information will change beliefs enough to influence decision-making. Through two experiments in the health domain we investigate (1) how degree of belief influences how people assess options, and (2) whether new information changes people's assessment of how reasonable those options are. Our results demonstrate the impact of incorrect beliefs on decision-making, and the difficulty of using causal models to correct these beliefs.

Causal explanations are important guides to understanding the world. While research suggests people prefer simple explanations, a seeming notable exception exists in the widespread endorsement of conspiracy theories. Researchers have described conspiracy theories as causally complex explanations of world events. We examined whether the lay public agrees with this assessment and sees conspiracy theories as complex explanations, as well as how perceptions of complexity relate to believability of these explanations. We tested publicly available (Experiment 1) and experimenter-generated (Experiment 2) conspiracy theories, alongside fact-based explanations for the same events. We asked participants to rate the complexity of each explanation, along with how believable they find the explanation. Participants across studies rated the conspiracy theory explanations as more complex. Interestingly, complexity was positively correlated with believability of the conspiracy theory, but not fact-based, explanations. We discuss what these findings suggest for the causal explanation field and our understanding of conspiracy theories.

Many methods exist to learn causal models from data, as causal relationships form the basis for successful actions. These methods are frequently evaluated based on the completeness of the models they can infer. Yet, there is a gap between the highly complete and potentially complex models algorithms can learn and the types of information people can use successfully to make decisions. To address this we conduct two experiments to understand how the size and features of causal models influence how well they can be used for decision-making. In Experiment 1 we systematically vary model size for a series of topics, finding that there is a negative and linear relationship between causal model size and decision accuracy. In Experiment 2 we examine how model structure influences decisions, varying whether the models include feedback loops, again finding that smaller models lead to better choices, and that feedback loops are also beneficial.