Beneath the Surface of Unfounded Belief Systems: An Extended Test of the Dimensionality Hypothesis.

This study tests the dimensionality hypothesis, according to which diverse epistemically unfounded beliefs share a single underlying liability in addition to domain-specific components. We focus on conspiracy, paranormal, pseudoscientific, religious, and pseudo-profound ("bullshit") beliefs and examine whether their covariance structure is best captured by a common factor plus residual domain-specific factors. Using a nationally representative Russian sample (N = 1268), we modeled the five belief domains with competing confirmatory factor analysis models. We compared one-factor, correlated factors, higher order, and bifactor specifications, with the bifactor model prespecified as the focal test of the dimensionality hypothesis. We then related the general and domain-specific factors to cognitive and motivational antecedents. A prespecified bifactor solution, in which a general factor explained about half of the common variance while specific factors captured residual domain structure, provided the best fit. Conventional domain scores may systematically confound the general liability with domain-specific variance, potentially distorting cross-domain comparisons and inferences about domain-specific antecedents. Some cognitive and motivational antecedents emerged as central correlates of the general factor, whereas conspiracy, religious, pseudoscientific, paranormal, and pseudo-profound beliefs each showed distinct additional profiles. The findings support a dimensional view of epistemically unfounded beliefs anchored in a common cognitive-motivational architecture. We discuss the general factor as potentially reflecting an evolved predisposition favoring Type I errors (the "smoke detector principle") and archaic magical thinking, plausibly implemented at the proximate level in low-dimensional psychological "conceptual spaces" that align heterogeneous beliefs along a shared axis of receptivity to epistemically unfounded claims. Explicitly modeling this structure is crucial for valid measurement, comparison, and targeted intervention across epistemically unfounded belief domains, as opposed to relying on undifferentiated composite scores.