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Cognitive Principles for Information Management: The Principles of Mnemonic Associative Knowledge (P-MAK)

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Abstract

Information management systems improve the retention of information in large collections. As such they act as memory prostheses, implying an ideal basis in human memory models. Since humans process information by association, and situate it in the context of space and time, systems should maximize their effectiveness by mimicking these functions. Since human attentional capacity is limited, systems should scaffold cognitive efforts in a comprehensible manner. We propose the Principles of Mnemonic Associative Knowledge (P-MAK), which describes a framework for semantically identifying, organizing, and retrieving information, and for encoding episodic events by time and stimuli. Inspired by prominent human memory models, we propose associative networks as a preferred representation. Networks are ideal for their parsimony, flexibility, and ease of inspection. Networks also possess topological properties—such as clusters, hubs, and the small world—that aid analysis and navigation in an information space. Our cognitive perspective addresses fundamental problems faced by information management systems, in particular the retrieval of related items and the representation of context. We present evidence from neuroscience and memory research in support of this approach, and discuss the implications of systems design within the constraints of P-MAK’s principles, using text documents as an illustrative semantic domain.

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Notes

  1. However the archealogical record suggests a stoic state of mind, with little evidence of imaginative artistry. Apparently the lesser working memory of Neanderthalensis eventually allowed the incremental innovation of Homo Sapiens to pull ahead.

  2. The mechanistic principles also apply to the constraints of brain function, as suggested by Marr (1982), but such an analysis is separate from our focus on improved human–machine interaction.

  3. Information management is primarily concerned with long-term storage and retrieval. Thus we do not directly discuss short-term working memory (Baddeley and Hitch 1974), which has more to do with transient attentional processes that assemble cues to process retrievals.

  4. As “an enlarged intimate supplement” to memory, memex is the earliest system design to embody the crucial human-centred principles of associationism and persistence.

  5. A possible method of coding asymmetry of relation between words is mentioned briefly in (Landauer and Dumais 1997), although not elaborated.

  6. Different knowledge structures are optimal for different types of representation. For instance, as Bayes nets provide a compact representation of joint probability distributions, so the network representation of P-MAK provides a compact representation of semantic and contextual relations.

  7. “Network thinking is poised to invade all domains of human activity and most fields of human inquiry. It is more than another helpful perspective or tool. Networks are by their very nature the fabric of most complex systems, and nodes and links deeply infuse all strategies aimed at approaching our interlocked universe.” (Barabási 2002, p. 222).

  8. Attributes could themselves be represented as nodes, as in (Jones 1986) and following on our use of index nodes to represent cues. However, for illustrative purposes we use a simpler formulation of nodes with self-contained semantics here; a simple device such as an inverted index (Salton and McGill 1983) can then be used to retrieve all nodes that contain a given attribute.

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Acknowledgements

We are grateful to Joel Lanir and Heidi Lam for their thoughtful insight and helpful comments. This paper was supported in part by NSERC postgraduate scholarships PGS B-267320 and IPS 2-268129.

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  1. Department of Computer Science, University of British Columbia, 2366 Main Mall, Vancouver, BC, Canada, V6T 1Z4

    Michael Huggett, Holger Hoos & Ron Rensink

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Huggett, M., Hoos, H. & Rensink, R. Cognitive Principles for Information Management: The Principles of Mnemonic Associative Knowledge (P-MAK). Minds & Machines 17, 445–485 (2007). https://doi.org/10.1007/s11023-007-9080-4

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