Feature-sampling and random-walk models of individual-stimulus recognition

Traditional process models of old-new recognition have not addressed differences in accuracy and response time between individual stimuli. Two new process models of recognition are presented and applied to response time and accuracy data from 3 old-new recognition experiments. The 1st model is derived from a feature-sampling account of the time course of categorization, whereas the 2nd model is a generalization of a random-walk model of categorization. In the experiments, a new technique was used, which yielded reliable individual-stimulus data through repeated presentation of structurally equivalent items. The results from the experiments showed reliable differences in accuracy and response times between stimuli. The random-walk model provided the better account of the results from the 3 experiments. The implications of the results for process models of recognition are discussed.