Cognitive Science

History of Semantic Networks

Matt Webb points to this great paper describing 6 different types of semantic networks. Applicable to the ontologists among us, semantic networks also make great diagram fodder. Not sure what a semantic network is?

A semantic network or net is a graphic notation for representing knowledge in patterns of interconnected nodes and arcs. Computer implementations of semantic networks were first developed for artificial intelligence and machine translation, but earlier versions have long been used in philosophy, psychology, and linguistics.

What is common to all semantic networks is a declarative graphic representation that can be used either to represent knowledge or to support automated systems for reasoning about knowledge.

Door of Perception 7: Flow

there is literally a wealth of fascinating presentation material and cutting edge theory of experience design to be found in the collected talks available online from the Doors 7: Flow conference

this one by London's Design Council on Humanising Technology was particularly intriguing

Design Council's Humanising Technology effort

Design creates space for common language between disciplines.

One company we are working with is developing highly complex software for large businesses in the energy industry. The company moved from being knowledge consultants in the industry to developers of a new technology that will allow real time financial modelling. Even before they have a UI the small, highly specialised team realised that there was no shared representation of the technology and therefore different perceptions of the benefits it will bring.

Cognitive Models for Web Design

Tanya Rabourn discusses information foraging, a theory that attempts to explain human information seeking behavior based on the food foraging theory from biology and anthropology. According to Pirolli and Card, "Information foraging theory analyzes trade-offs in the value of information gained against the costs of performing activity in human-computer interaction tasks." The advantage in using this theory as the basis for modeling information seeking behavior comes in the form of understanding users' cognitive mapping of knowledge and knowledge relationships and understanding attributes of information navigation such as scent. Tanya discusses 3 new tools which would benefit this area of study: 1) ACT-R, which uses network modeling of knowledge to model interaction, 2) analyzing user paths from web server log data and creating user profiles from that analysis, and 3) collaborative filtering or foraging for information groups.

Tanya's essay gives a concise summary of the literature and discusses some new methods for applying the theory. My eureka moment came last night when I saw James demonstrate his latest OmniGraffle experiments, which use web server logs to to create what he calls self-organizing site maps -- diagrams that show paths traveled between nodes/pages on a site to reveal real users' information seeking behavior. In a sense the relationships that emerge reveal the collective user base's cognitive map. It can be used to show where information scent was weak or strong and where content structure doesn't map to user peceptions.

I've been wanting a better way to test the information architecture of sites based on actual information use, and it's not until I read Tanya's essay and saw the visualization that James came up with that my brain was able to churn on this concept. It's nice to know smart and creative people.

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