Cue abstraction and ideal prototype abstraction in estimation tasks

Abstract

This study investigates abstracted processes and introduces a new prototype abstraction model adapted to estimation tasks. This prototype abstraction model assumes that the processing of whole exemplar patterns supports the detection of the underlying statistics necessary for the abstraction of two extreme prototypes on the continuous criterion dimension of the task. The prototypes are stored in memory as valid reference points for future similarity-based judgments. This prototype model was compared with the cue abstraction model, which assumes that people abstract cue weights in learning and add the cue information from exemplars to infer their criterion values varying on the continuous dimension. This study hypothesises that the training mode and the number of exemplars in training interact and affect subsequent model performance at test. The results from an experiment confirmed this hypothesis and showed that observational training supports an efficient prototype abstraction and feedback training supports an efficient cue abstraction.