Application Knowledge I

Application knowledge refers to recognizing whether novel objects or actions are examples of a generalization [1]. Cognitive psychologists also refer to generalizations by such terms as; mental models [2], schema or schemata [3], scripts [4]. production systems [5], and cognitive representations [6]. Application knowledge involves two implicit and unobservable cognitive processes; (a) constructing and encoding generalizations in long-term memory for patterns of information perceived in the environment, and the transfer activity [7] of (b) matching previously encoded generalizations with new examples from the environment.

The two implicit cognitive processes in application knowledge are explicitly illustrated in the differing behaviors of two groups of chess players, ordinary players (novices) and grandmasters (experts) [8]. During chess exhibitions an expert grandmaster plays against 25 or 30 novice ordinary players at the same time and beats all of them. The expert walks up to a partially played chess board, and within seconds moves a chess piece, and then goes on to the next player. The novice who was just moved against takes a number of minutes to decide on the next move.

An early assumption was that experts were geniuses. But that was found not to be the case. A study of chess players ranging from amateurs to grandmasters found no connection between their playing strengths and their visual-spatial abilities, as measured by shape-memory tests [9]. Experts are simply able to use a rapid, generalization-guided perception to recognize the particular pattern of key chess position they see on the chess board. They then make the chess move they already know to be associated with that key position. It has been estimated that grandmasters possess encoded generalizations for roughly 50,000 to 100,000 key chess-board positions [10]. Stated another way, chess experts have encoded mental models of thousands of key chess positions. And they are able to quickly match each mental model they have encoded in long-term memory with an example of it on a partially played chess board. Each key position is associated with a critical next chess move, which they are able to make within seconds. Novices, by contrast, have typically encoded generalizations for only a few key chess positions.

Another early assumption was that experts excel in their memorization ability. That was also found not to be the case. In a memory test novices and experts were shown drawings of key chess positions on boards and given up to 30 seconds to memorize each of them, and then without the drawings to reconstruct the chess positions from memory. Novices were unable to recall more than a few details of the positions on the chess board. Experts were able to look at a drawing for only a few seconds and later recall all the chess positions perfectly. Experts remembered more positions because they recognized examples of key chess positions they had already encoded in their long-term memories. Novices possessed little or no knowledge of key chess positions from which to draw upon. On the other hand, when both groups were shown drawings of randomly placed chess positions, with none being key chess positions you would find in an actual game, there was no difference in their ability to remember. The experts remembered no better because they did not recognize any examples of the key chess positions they knew, so they were unable to utilize their vast knowledge base of chess positions.

Surprisingly, the preponderance of evidence indicates that experts in any field are made, not born. Motivation is more important than innate ability. Expertise is the result of highly motivated and persistent engagement in an effective instructional environment.

Similar differences in the application knowledge of novices and experts have been found in people of differing ages and in such diverse fields as playing bridge [11], playing baseball [12], playing basketball [13], electronics [14], computer programming [15], physics [16], general science [17], mathematics [18], reading [19], writing [20], political science [21], legal analysis [22], biology [23], and environmental science [24]. Even those fields that are commonly attributed to naturally endowed artistic skill, inspiration and genus, such as music and painting, have been found to require years of highly motivated and persistent study in order to acquire the application knowledge that is necessary for high levels of competent artistic performance [25].

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1. Anderson, R.C., & Faust, G.W. (1973). Educational psychology: The science of instruction and learning. New York: Harper & Row.

2. Johnson-Laird, P.N. (1983). Mental models. Cambridge, MA: Harvard University Press.

3. (For example). Rumelhardt, D.E., & Ortony, A. (1976) The representation of knowledge in memory. In R.C. Anderson, R.J Spiro, & W.E. Spiro, & W.E. Montague (Eds.), Schooling and the acquisition of knowledge. Hillsdale, NJ: Erlbaum.

4. (For example). Walker, C.H., & Yekovich, F.R. (1987). Activation and use of script-based antecedents in anaphoric reference. Journal of Memory and Language, 26, 673-691.

5. Anderson, J.R. (1983). The architecture of cognition. Cambridge, MA: Harvard University Press.

6. Kaput, J.J. (1985) Representation and problem solving: Methodological issues related to modeling In E.A. Silver (Ed.), Teaching and learning mathematical problem solving: Multiple research perspectives (pp. 381-398). Hillsdale, NJ: Erlbaum.

7. Cormier, S.M., & Hagman, J.D. (1987). (Eds.), Transfer of learning: Contemporary research and application. New York: Academic Press.

8. Simon, H.A., & Chase, W.G. (1973). Skill in chess. American Scientist, 61, 394-403.

9. Gobet, F., Voogt, A., & Retschizki, J. (2004). Moves in mind: The psychology of board games. Psychology Press.

10. Ross, P.E. (August, 2006). The expert mind. Scientific American, 295, 64-71.

11. Charness, N. (1979). Components of skill in bridge. Canadian Journal of Psychology, 33, 1-16.

12. Chiesi, H.L., Spillich, G.J., & Voss, J.F. (1979). Acquisition of domain-related information in relation to high and low domain knowledge. Journal of Verbal Learning and Verbal Behavior, 18, 257-273.

13. Allard, F. & Burnett, N. (1985). Skill in sport. Canadian Journal of Psychology, 39, 294-312.

14. Egan, D.E., & Schwartz, B.J. (1979). Chunking in recall of symbolic drawings. Memory and Cognition, 7, 149-158.

15. (For example). Adelson, B. (1985). Comparing natural and abstract categories: A case study from computer science. Cognitive Science, 9, 417-430.

16. (For example). McDermott, L. July, 1984). Research in conceptual understanding in mechanics. Physics Today, 24-32.

17 (For example). Goggo, C., & Chi, M.T.H. (1986). How knowledge is structured and used by expert and novice children. Cognitive Development, 1, 221-237.

18. (For example). Schoenfeld, A.H. (1986). On having and using geometric knowledge. In J. Hiebert (Ed.), Conceptual and procedural knowledge: The case of mathematics (pp. 225-264). Hillsdale, NJ: Erlbaum.

19. Lesgold, A.M., & Resnick, L.B. (1982). How reading difficulties develop: Perspective from a longitudinal study. In J.P. Das, R. Mulcahy, & A. E. Walls (Eds.), Theory and research in learning and learning disability. New York: Plenum.

20. Bruce, B., Collins, A. Rubin, A.D., & Gentner, D. (1982). Three perspectives on writing. Educational Psychologist, 17, 131--145.

21. Voss, J.F., Greene, T.R., Post, T.A., & Penner, B.C. (1983). Problem solving skill in social science. In G.H. Bower (Ed.), The psychology of learning and motivation: Advances in research and theory (Vol. 17, pp.05-323). New York: Academic Press.

22. Lunderberg, M.A. (1987). Metacognitive aspects of reading comprehension: Studying understanding in legal case analysis. Reading Researach Quarterly, 22, 407-432.

23. (For example). Fisher, K.M. (1985). A misconception in biology: Amino acids and translation. Journal of Research in Science Teaching, 22, 53-62.

24. Tutor;, M.T. (1992). Expert and novice differences in strategies to problem solve an environmental issue. Contemporary Educational Psychology, 17, 329-339.

25. Hayes, J.R. (1985). Three problems in teaching general skills. In S.F. Chipman, J.W. Segal, & R. Glaser (Eds.), Thinking and learning skills (Vol. 1, pp. 391-406). Hillsdale, NJ: Erlbaum.