Presentation order and recognition of categorically related examples

Two experiments are described in which different groups of participants saw the same examples in different orders and then were given an old-new recognition test. The learning and test examples were created from different combinations from four binary-valued dimensions. One order (small change) was constructed to maximize the similarity between successive examples, and the other order (large change) minimized the similarity across successive examples. The small change condition was consistently associated with better old-new recognition than the large change condition was. These results are discussed in terms of exemplar-guided encoding and models of category generalization.