How framing statistical statements affects subjective veracity: validation and application of a multinomial model for judgments of truth.

Extending the well-established negativity bias in human cognition to truth judgments, it was recently shown that negatively framed statistical statements are more likely to be considered true than formally equivalent statements framed positively. However, the underlying processes responsible for this effect are insufficiently understood. Therefore, a multinomial processing tree model is herein proposed to distinguish between differences in (a) knowledge or (b) response bias that may account for the framing effect. Three model validation experiments supported the psychological interpretability of model parameters. Model application revealed that the framing effect can be considered a bias: Given insufficient knowledge, individuals more likely guessed "true" when faced with a negatively framed statistical statement. The probability of conclusive knowledge, however, remained constant across frames. In summary, this article puts forwards and validates a formal model that can be used more generally to investigate processes underlying truth judgments. Based on this model, it is herein shown that one particular phenomenon - framing effects observed for statistical statements - can be considered a response bias, rather than the upshot of differential knowledge.