Current versions of distributed models have difficulty in accounting for the representation of order information in matching tasks. In this article, experiments are presented that allow discrimination between physical and ordinal representations of ordinal information, discrimination between position-dependent codes and context-sensitive codes, and generalization of the results of matching tasks from strings of letters to long-term memory for triples of words. Data from these experiments constrain the kinds of models that can be developed to account for matching and order, and present problems for several current memory models. Including connectionist models. Suggestions are made for modifications of these models to account for the results from matching tasks.

The mnemonic discrimination task (MDT) is a widely used cognitive assessment tool. Performance in this task is believed to indicate an age-related deficit in episodic memory stemming from a decreased ability to pattern-separate among similar experiences. However, cognitive processes other than memory ability might impact task performance. In this study, we investigated whether nonmnemonic decision-making processes contribute to the age-related deficit in the MDT. We applied a hierarchical Bayesian version of the Ratcliff diffusion model to the MDT performance of 26 younger and 31 cognitively normal older adults. It allowed us to decompose decision behavior in the MDT into different underlying cognitive processes, represented by specific model parameters. Model parameters were compared between groups, and differences were evaluated using the Bayes factor. Our results suggest that the age-related decline in MDT performance indicates a predominantly mnemonic deficit rather than differences in nonmnemonic decision-making processes. In addition, this mnemonic deficit might also involve a slowing in processes related to encoding and retrieval strategies, which are relevant for successful memory as well. These findings help to better understand what cognitive processes contribute to the age-related decline in MDT performance and may help to improve the diagnostic value of this popular task.