Perceptions of Explanation Completeness Help Decrease Knowledge Overestimation

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

Naïve Beliefs About Intervening on Causes and Symptoms in the Health Domain

In two experiments we tested people’s naïve beliefs about where interventions act in real-world causal systems. We provided people with a description of a novel health condition that could be treated by two different treatments, a medication and a lifestyle modification. Participants judged a medication as acting on the symptoms of a disorder instead of the cause of the disorder, while a lifestyle modification was seen as acting on both the cause and the symptoms of a health condition (Experiment 1). These results held despite participants rating both treatments as effective. Providing information about the specific causal mechanism by which a treatment could work did not increase beliefs about a medication’s ability to target the cause of a disorder (Experiment 2). Implications for understanding of everyday causal interventions and health treatments is discussed

Tell me something I don’t know: How perceived knowledge influences the use ofinformation during decision making

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

Less is more: information needs, information wants, and what makes causal models useful

Abstract Each day people make decisions about complex topics such as health and personal finances. Causal models of these domains have been created to aid decisions, but the resulting models are often complex and it is not known whether people can use them successfully. We investigate the trade-off between simplicity and complexity in decision making, testing diagrams tailored to target choices (Experiments 1 and 2), and with relevant causal paths highlighted (Experiment 3), finding that simplicity or directing attention to simple causal paths leads to better decisions. We test the boundaries of this effect (Experiment 4), finding that including a small amount of information beyond that related to the target answer has a detrimental effect. Finally, we examine whether people know what information they need (Experiment 5). We find that simple, targeted, information still leads to the best decisions, while participants who believe they do not need information or seek out the most complex information performed worse

The Transferability of Explanation-Induced Knowledge Reassessment

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

Explanation Hubris and Conspiracy Theories: A Case of the 2016 Presidential Election

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

Transferring Novel Causal Knowledge

Knowledge of cause and effect allows people to navigate and understand the complex systems of the world. Despite the importance of causal knowledge to everyday reasoning, little is known about how people transfer causal knowledge learned in one situation to novel contexts. In two experiments, we examine when people choose to generalize two types of causal knowledge, causal mechanisms (Experiment 1) and causal strength (Experiment 2), across various domains. We find that people willingly transfer causal knowledge to novel contexts when the entities in those contexts share high categorical relatedness with the source of the causal knowledge. The extent to which people are willing to transfer causal knowledge decreases as category similarity decreases. We discuss future research that could delineate the boundaries of causal transfer