Modelling order within associations in symmetric models of association memory.

Despite many examples of order-sensitive paired associates (e.g., FISH HOOK), the study of association memory (e.g., AB, CD) has been theoretically isolated from the study of order memory (e.g., ABCD). As a result, formal models of association memory are poor at accounting for within pair order (AB vs. BA), and either predict that order judgments of a retrieved pair should be at chance or perfect. Behaviour contradicts both predictions, when the pair can be recalled, order judgment is above chance, but well below perfect. We tested four separate order encoding mechanisms that could be added to existing convolution-based models, which otherwise predict chance order judgment performance, where pair order is encoded as: 1) positional item features, 2) position-specific permutations of item features, 3) position-item associations, and 4) adding position vectors to items. All models achieved close fits to aggregate order recognition data, without compromising associative symmetry. Although published models are unable to capture the relationship between memory for associations and their constituent order, multiple promising enhancements to convolution models are feasible.