Judgment and Response Processes across Two Knowledge Domains

Wallsten and Gonzalez-Vallejo (1994) developed the Stochastic Judgment Model to account for true-false judgment and response processes in a single well-defined knowledge domain. This paper generalizes the model to a four-category rating task that encompasses two knowledge domains simultaneously. It then applies the model to an experiment in which Ph.D. students in history and English literature rated confidence in the truth of statements in both domains, and also decided which statement within a pair consisting of one from each domain was more likely true. Constrained versions of the general model fit the rating data very well and accurately predicted the pair-comparison (PC) choices. The results suggest that (a) the mean distance between the true and false statement distributions of confidence was greater in the better known domain; (b) judged confidence variability is greater in the domain of greater knowledge; while simultaneously (c) criterion variability is constant across domains; (d) the extreme response criteria are located symmetrically around the central one; which (e) is located to yield the usual bias to call statements true. Finally, cross-domain PC choices were very well predicted by assuming that respondents judged only the statement in the single domain they knew better and not well predicted by the more common assumption that they compare their levels of confidence in the two statements. Implications for the underlying cognitive processes are discussed including the effects of expertise.