Cognitive Load and Individual Differences as Drivers of Health-Related Misinformation: A Signal Detection Approach

The global spread of health-related misinformation during the COVID-19 pandemic has highlighted the need to understand the psychological mechanisms that make individuals susceptible to health-related misinformation. This study examined the impact of cognitive load and individual differences on susceptibility to COVID-19 misinformation using a Signal Detection Theory framework. A sample of 634 university students evaluated true and false COVID-19 headlines under working memory load and no load conditions, while also completing standardized assessments of cognitive reflection, epistemically unwarranted beliefs, naıve skepticism, and bullshit receptivity. Results showed that, although the working memory load manipulation successfully impaired recall and increased response time, it did not affect participants’ tendency to accept false information as true. In contrast, individual-difference variables robustly predicted misinformation susceptibility. Specifically, beliefs in pseudoscience, epistemically unwarranted beliefs, naıve skepticism and bullshit receptivity showed moderate positive associations with false-alarm rates and negative associations with discrimination sensitivity. Cognitive reflection was negatively associated with false-alarm rates and positively with discrimination sensitivity. Only small associations were found between individual-difference dimensions and response bias. These findings suggest that stable cognitive and epistemic dispositions, rather than momentary cognitive constraints, play a more significant role in shaping people’s ability to distinguish between accurate and false health information.