PROCESS MODELS AND STOCHASTIC THEORIES OF SIMPLE CONCEPT FORMATION
PROCESS MODELS AND STOCHASTIC THEORIES OF SIMPLE CONCEPT FORMATION
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DOI：
10.1016/0022-2496(67)90052-1
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发表时间：
1967-01-01
影响因子：
1.8
通讯作者：
SIMON, HA
中科院分区：
文献类型：
--
作者：
GREGG, LW;SIMON, HA
一类信息处理模型，在计算机编程语言中表示，构建了一个概念获得实验先前研究的鲍尔和Trabasso。鲍尔和特拉巴索的随机理论可以从过程模型中正式推导出来，但过程模型的自由度更小，比随机理论在更广泛的实验范围内做出更具体的预测，因此在波普尔的意义上更具有普遍性、精确性和简约性。正式的过程模型被证明是有用的发现不一致和未说明的假设，在非正式的描述的心理过程的随机理论。随机理论的“细粒”统计数据的拟合被证明是独立的理论的心理内容。
A class of information-processing models, stated in computer programming language, is constructed for a concept attainment experiment previously studied by Bower and Trabasso. The stochastic theory of Bower and Trabasso can be derived formally from the process models, but the process models, with fewer degrees of freedom, make more specific predictions over a wider range of experiments than the stochastic theory[long dash]hence are more universal, precise, and parsimonious in the sense of Popper. The formal process models are shown to be useful in discovering inconsistencies and unstated assumptions in informal descriptions of the psychological processes underlying the stochastic theory. The fit of the "fine-grain" statistics of the stochastic theory to the data is shown to be independent of the psychological content of the theory.