阶段变化模型解释了发展阶段的平均速率及其与预测平均阶段(“智能”)的关系。

M. Commons, L. S. Miller, Sagun Giri
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引用次数: 13

摘要

许多不同的先前测量“聪明”的方法导致了层次复杂性模型(MHC),这是一种与上下文无关的行为复杂性的新皮亚杰数学模型。它提供了一种根据层次复杂性对任务进行分类的方法。使用层次复杂性模型,本研究考察了阶段变化率的差异如何导致70岁成年人达到的最高平均阶段(“聪明”)的差异。平均发展阶段(“聪明”)被证明是由年龄的对数预测的,r = 0.79。它使用Colby, Kohlberg, Gibbs, Lieberman(1983)的数据来检验模型。它还预测,在成年期平均有一个发展阶段。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A model of stage change explains the average rate of stage of development and its relationship to the predicted average stage ("smarts")
A number of different previous methods for measuring “smarts” have led to the model of hierarchical complexity (MHC), a context free neo-Piagetian mathematical model of behavioral complexity. It provides a way to classify tasks as to their hierarchical complexity. Using the model of hierarchical complexity, this study examines how differences in rate of stage change results in a difference in the highest average stage (smarts”) attained by 70 year old adults. The average stage of development (“smarts”) was shown to be predicted by the log of age with an r = .79. It uses data from Colby, Kohlberg, Gibbs, Lieberman (1983) to test the model. It also predicts that on the average there is one stage of development during adulthood.
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