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Features of distributed representations for tree-structures: A study of RAAM

This paper presents an in-depth analysis of properties of patterns, generated by the Recursive Auto-Associative Memory, based on the idea that representational features can be detected by a classification network. The intension of this analysis is to examine the actual reasons for the success of connectionist processes acting on super-positional activity vectors generated in the fashion described. We show that the structure supplied during training is maintained and is extractable from the generated pattern. Further, we show that the influence of the actual constituents in the structures supplied during training is not necessarily available, in the generated patterns, for holistic processing. The outlook for holistic processing is therefore limited unless new forms can be found which take into account, what Sharkey and Jackson call, `whole net' representations.