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

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 worldthat 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.

A holographic associative memory recommender system

“…This paper describes a somewhat different approach to recommendation, one that is based on a cognitive model of associative memory -Dynamically Structured Holographic Memory (DSHM). We were motivated by the intuition that applying a cognitive model of memory could enhance the effectiveness of an information management system such as a recommender by making it behave more like a human expert [8]. For example, often the best way to get a movie recommendation is to ask the video store clerk to recommend a movie based on what you have enjoyed viewing recently.…”