As the Internet has become a nearly ubiquitous resource for acquiring knowledge about the world, questions have arisen about its potential effects on cognition. Here we show that searching the Internet for explanatory knowledge creates an illusion whereby people mistake access to information for their own personal understanding of the information. Evidence from 9 experiments shows that searching for information online leads to an increase in self-assessed knowledge as people mistakenly think they have more knowledge "in the head," even seeing their own brains as more active as depicted by functional MRI (fMRI) images.

Does expertise within a domain of knowledge predict accurate self-assessment of the ability to explain topics in that domain? We find that expertise increases confidence in the ability to explain a wide variety of phenomena. However, this confidence is unwarranted; after actually offering full explanations, people are surprised by the limitations in their understanding. For passive expertise (familiar topics), miscalibration is moderated by education; those with more education are accurate in their self-assessments (Experiment 1). But when those with more education consider topics related to their area of concentrated study (college major), they also display an illusion of understanding (Experiment 2). This "curse of expertise" is explained by a failure to recognize the amount of detailed information that had been forgotten (Experiment 3). While expertise can sometimes lead to accurate self-knowledge, it can also create illusions of competence.

Across many different contexts, individuals consult customer ratings to inform their purchase decisions. The present studies document a novel phenomenon, dubbed "the binary bias," which plays an important role in how individuals evaluate customer reviews. Our main proposal is that people tend to make a categorical distinction between positive ratings (e.g., 4s and 5s) and negative ratings (e.g., 1s and 2s). However, within those bins, people do not sufficiently distinguish between more extreme values (5s and 1s) and less extreme values (4s and 2s). As a result, people's subjective representations of distributions are heavily impacted by the extent to which those distributions are imbalanced (having more 4s and 5s vs. more 1s and 2s). Ten studies demonstrate that this effect has important consequences for people's product evaluations and purchase decisions. Additionally, we show this effect is not driven by the salience of particular bars, unrealistic distributions, certain statistical properties of a distribution, or diminishing subjective utility. Furthermore, we demonstrate this phenomenon's relevance to other domains besides product reviews, and discuss the implications for existing research on how people integrate conflicting evidence.

One of the mind’s most fundamental tasks is interpreting incoming data and weighing the value of new evidence. Across a wide variety of contexts, we show that when summarizing evidence, people exhibit a binary bias: a tendency to impose categorical distinctions on continuous data. Evidence is compressed into discrete bins, and the difference between categories forms the summary judgment. The binary bias distorts belief formation—such that when people aggregate conflicting scientific reports, they attend to valence and inaccurately weight the extremity of the evidence. The same effect occurs when people interpret popular forms of data visualization, and it cannot be explained by other statistical features of the stimuli. This effect is not confined to explicit statistical estimates; it also influences how people use data to make health, financial, and public-policy decisions. These studies ( N = 1,851) support a new framework for understanding information integration across a wide variety of contexts.