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Misinformation is widespread, but only some people accept the false information they encounter. This raises two questions: Who falls for misinformation, and why do they fall for misinformation? To address these questions, two studies investigated associations between 15 individual-difference dimensions and judgments of misinformation as true. Using Signal Detection Theory, the studies further investigated whether the obtained associations are driven by individual differences in truth sensitivity, acceptance threshold, or myside bias. For both political misinformation (Study 1) and misinformation about COVID-19 vaccines (Study 2), truth sensitivity was positively associated with cognitive reflection and actively open-minded thinking, and negatively associated with bullshit receptivity and conspiracy mentality. Although acceptance threshold and myside bias explained considerable variance in judgments of misinformation as true, neither showed robust associations with the measured individual-difference dimensions. The findings provide deeper insights into individual differences in misinformation susceptibility and uncover critical gaps in their scientific understanding. 

Recent years have seen a surge in research on why people fall for misinformation and what can be done about it. Drawing on a framework that conceptualizes truth judgments of true and false information as a signal-detection problem, the current article identifies three inaccurate assumptions in the public and scientific discourse about misinformation: (1) People are bad at discerning true from false information, (2) partisan bias is not a driving force in judgments of misinformation, and (3) gullibility to false information is the main factor underlying inaccurate beliefs. Counter to these assumptions, we argue that (1) people are quite good at discerning true from false information, (2) partisan bias in responses to true and false information is pervasive and strong, and (3) skepticism against belief-incongruent true information is much more pronounced than gullibility to belief-congruent false information. These conclusions have significant implications for person-centered misinformation interventions to tackle inaccurate beliefs. 

Researchers across many disciplines seek to understand how misinformation spreads with a view toward limiting its impact. One important question in this research is how people determine whether a given piece of news is real or fake. In the current article, we discuss the value of signal detection theory (SDT) in disentangling two distinct aspects in the identification of fake news: (a) ability to accurately distinguish between real news and fake news and (b) response biases to judge news as real or fake regardless of news veracity. The value of SDT for understanding the determinants of fake-news beliefs is illustrated with reanalyses of existing data sets, providing more nuanced insights into how partisan bias, cognitive reflection, and prior exposure influence the identification of fake news. Implications of SDT for the use of source-related information in the identification of fake news, interventions to improve people’s skills in detecting fake news, and the debunking of misinformation are discussed.