Cognitive Principles for Information Management: The Principles of Mnemonic Associative Knowledge (P-MAK) [Book Review]
David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
Minds and Machines 17 (4):445-485 (2007)
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
|Keywords||Information management Memory prosthesis Associationism Semantic similarity Co-occurrence Spatio-temporal context Episodic events Associative networks Spreading activation|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library|
References found in this work BETA
Jerry A. Fodor (1975). The Language of Thought. Harvard University Press.
George Lakoff (1987). Women, Fire and Dangerous Thing: What Catergories Reveal About the Mind. University of Chicago Press.
Noam Chomsky (1965). Aspects of the Theory of Syntax. The MIT Press.
Nelson Cowan (2001). The Magical Number 4 in Short-Term Memory: A Reconsideration of Mental Storage Capacity. Behavioral and Brain Sciences 24 (1):87-114.
David E. Meyer & Roger W. Schvaneveldt (1971). Facilitation in Recognizing Pairs of Words: Evidence of a Dependence Between Retrieval Operations. Journal of Experimental Psychology 90 (2):227.
Citations of this work BETA
No citations found.
Similar books and articles
Vinod Goel (1991). Notationality and the Information Processing Mind. Minds and Machines 1 (2):129-166.
Marc W. Howard, Karthik H. Shankar & Udaya K. K. Jagadisan (2011). Constructing Semantic Representations From a Gradually Changing Representation of Temporal Context. Topics in Cognitive Science 3 (1):48-73.
Patrick Allo (2008). Formalising the 'No Information Without Data-Representation' Principle. In P. Brey, A. Briggle & K. Waelbers (eds.), Current Issues in Computing and Philosophy. IOS Press
Jerry A. Fodor (1986). Information and Association. Notre Dame Journal of Formal Logic 27 (July):307-323.
Christine P. Ries (2001). Enterprise Risk Management: Applications of Economic Modeling and Information Technology. Mind and Society 2 (2):1-8.
Brian Fisher, Tera Marie Green & Richard Arias-Hernández (2011). Visual Analytics as a Translational Cognitive Science. Topics in Cognitive Science 3 (3):609-625.
Nick Bostrom (forthcoming). Smart Policy: Cognitive Enhancement and the Public Interest. In Julian Savulescu, Ruud ter Muelen & Guy Kahane (eds.), Enhancing Human Capabilities. Wiley-Blackwell
Orlin Vakarelov (2010). Pre-Cognitive Semantic Information. Knowledge, Technology & Policy 23 (2):193-226.
Added to index2009-01-28
Total downloads18 ( #172,177 of 1,777,407 )
Recent downloads (6 months)3 ( #168,647 of 1,777,407 )
How can I increase my downloads?