2002 Fiscal Year Final Research Report Summary
Mechanisms of performance change in long-term practice
| Project/Area Number |
12680401
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | Chukyo University |
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Principal Investigator |
KIMURA Izumi Chukyo University, School of Computer and Cognitive Sciences Professor, 情報科学部, 教授 (50015525)
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| Project Period (FY) |
2000 – 2002
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| Keywords | Power Law of Practice / cognitive-motor skill / task time / long-term practice / paper-folding task / slumps / vantage points / vantage lines |
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| Research Abstract |
"The Power Law of Practice" claims that any set of practice data, if plotted on a log-log graph paper, makes a straight line.
In reality, however, the left-hand and the right-hand sides of such a graph tend to level off. Seibel(1964) and Newell and Rosenbloom(1981) attributed this phenomenon to "prior leaning" on the left, and "the human limits of performance" on the right, thus introducing two new adjustable parameters in the fitting.
Are they really necessary? I say "Probably no," on the basis of the data I obtained in a series of long-term practice experiments. I used a paper-folding (Japanese "origami") task with 13 participants, as well as two other tasks. In the 2000-2002 fiscal years, I obtained the following results.
1. All the practice curves I obtained included a regular set of wave patterns. The fittings based on the idea of Seibel et. al. might have erroneously interpreted the first and the last of the wavy patters as leveling-off of the data.
2. It is customary to think about practice data in terms of averages. I found that thinking about them in terms of a class of conspicuous new records, called "vantage points," gives more stable results, and hence better insights.
3. Call the set of line segments that connect the vantage points a "vantage line." The ratios of the task times divided by the heights of the vantage line at that location, when plotted on a graph, show a conspicuous regular patterns having high peaks and deep valleys. This pattern might have successfully extracted the "slump component" of the data.
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Research Products
(8 results)
