Information integration and the identification of stimulus noise and criterial noise in absolute judgment

Two main classes of theories have been proposed regarding range effects in unidimensional absolute-identification tasks. One class posits that as range is increased, criterial noise increases but stimulus noise remains constant. Another class posits increasing stimulus noise but constant criterial noise. In this study, an effort is made to help decide this issue. Multiple observations are used in several absolute-identification tasks of varying range. A stimulus integration model is proposed in which averaging takes place over stimulus internal representations, thereby reducing stimulus variance; on the other hand, it is assumed that criterial variance is unaffected by the number of observations. The model allows one to identify the relative amounts of stimulus noise and criterial noise inherent in observers' recognition judgments. The model yields good fits to data in several experiments, and it is concluded that both stimulus noise and criterial noise increase as range in the absolute-identification task is increased.