Statement verification: A stochastic model of judgment and response.

Abstract

This article presents a stochastic judgment model (SJM) as a framework for addressing a wide range of issues in statement verification and probability judgment. The SJM distinguishes between covert confidence in the truth of a proposition and the selection of an overt response. A series of experiments demonstrated the model's validity and yielded new results: Binary true-false responses were biased toward true relative to underlying judgment. Underlying judgment was also biased in that direction. Also, in a domain about which Ss had some knowledge, they discriminated true and false statements better when they compared complementary pairs before judging individual statements than when they performed those tasks in the opposite order