The Misinformation Receptivity Framework Political Misinformation and Disinformation as Cognitive Bayesian Inference Problems

Evaluating the truthfulness of new information is a difficult and complex task. Notably, there is currently no unified theoretical framework that addresses the questions of (1) how individuals discern whether political information is true or (deliberately) false, (2) under what conditions individuals are most susceptible to believing misinformation, and (3) how the structure of political and communicative environments skews cognitive processes of truth, discernment, and interpretation generation. To move forward, we propose the Misinformation Receptivity Framework (MRF). Building on Bayesian and probabilistic models of cognition, the MRF suggests that we can conceptualize misinformation receptivity as a cognitive inference problem in which the reliability of incoming misinformation is weighed against the reliability of prior beliefs. This "reliability-weighting" process can model when individuals adopt or reject misinformation, as well as the ways in which they creatively generate interpretations rather than passively discern truth versus falsehood. Moreover, certain communication contexts can lead people to rely excessively on incoming (mis)information or conversely to rely excessively on prior beliefs. The MRF postulates how such environmental properties can heighten the persuasiveness of different kinds of misinformation. For instance, the MRF predicts that noisy communication contexts, in which the reliability of inputs is ambiguous, make people susceptible to highly partisan and ideological misinformation or disinformation that amplifies their existing belief systems. By contrast, the MRF predicts that contextual instability renders people susceptible to misinformation that would be considered extreme or worldview-incongruent in conditions of stability. The MRF formally delineates the interactions between cognitive and communicative mechanisms, offering insights and testable hypotheses on when, how, and why different kinds of misinformation proliferate.