Cognoscente - Expertise - Research Related To Expertise

Research Related To Expertise

A number of computational models have been developed in cognitive science to explain the development from novice to expert. In particular, Herbert A. Simon and Kevin Gilmartin proposed a model of learning in chess called MAPP (Memory-Aided Pattern Recognizer). Based on simulations, they estimated that about 50,000 chunks (units of memory) are necessary to become an expert, and hence the many years needed to reach this level. More recently, the CHREST model (Chunk Hierarchy and REtrieval STructures) has simulated in detail a number of phenomena in chess expertise (eye movements, performance in a variety of memory tasks, development from novice to expert) and in other domains.

An important feature of expert performance seems to be the way in which experts are able to rapidly retrieve complex configurations of information from long-term memory. They recognize situations because they have meaning. It is perhaps this central concern with meaning and how it attaches to situations which provides an important link between the individual and social approaches to the development of expertise. Work on "Skilled Memory and Expertise" by Anders Ericsson and James J. Staszewski confronts the paradox of expertise and claims that people not only acquire content knowledge as they practice cognitive skills, they also develop mechanisms that enable them to use a large and familiar knowledge base efficiently.

Work on expert systems (computer software designed to provide an answer to a problem, or clarify uncertainties where normally one or more human experts would need to be consulted) typically is grounded on the premise that expertise is based on acquired repertoires of rules and frameworks for decision making which can be elicited as the basis for computer supported judgment and decision-making. However, there is increasing evidence that expertise does not work in this fashion. Rather, experts recognize situations based on experience of many prior situations. They are in consequence able to make rapid decisions in complex and dynamic situations.

In a critique of the expert systems literature Dreyfus & Dreyfus (2005) suggest:

If one asks an expert for the rules he or she is using, one will, in effect, force the expert to regress to the level of a beginner and state the rules learned in school. Thus, instead of using rules they no longer remember, as knowledge engineers suppose, the expert is forced to remember rules they no longer use. … No amount of rules and facts can capture the knowledge an expert has when he or she has stored experience of the actual outcomes of tens of thousands of situations.