Induction of relational schemas: Common processes in reasoning and complex learning
Induction of relational schemas: Common processes in reasoning and complex learning
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DOI：
10.1006/cogp.1998.0679
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发表时间：
1998-04-01
影响因子：
2.6
通讯作者：
Andrews, G
中科院分区：
文献类型：
--
作者：
Halford, GS;Bain, JD;Andrews, G
研究人员进行了五个实验，以测试受试者是否从特定实例中归纳出对任务结构的连贯表征，这种表征被称为关系图式。关系模式的属性包括：关系的显式符号、保持关系真实性的绑定、高阶关系的潜力、全方位访问、同构之间转移的潜力以及预测同构问题中不可见项的能力。但是，关系模式不一定以抽象形式编码。在五个实验中，将关系图式理论的预测与结构学习和其他非结构理论的预测进行了对比。在这些实验中，参与者被教授了一个由一组初始状态、运算符和结束状态实例组成的结构。给出了初始状态和操作符对，参与者必须预测正确的结束状态。成年受试者通过预测新同构问题的项目的能力，有效地实现了关系图式的归纳。所诱导的关系图式具有全向通达特性，能有效地向同构迁移，结构一致性对学习有很强的影响。观察到传统上与学习集文献相关的“学会学习”效应，并用关系图式归纳和结构映射组成的模型解释了长期存在的学习集习得之谜。倒置操作符后的表现好于转换到替代结构后，尽管后者在保留的配置关联数量方面与先前学习的任务有更多重叠。从关系模式的归纳和映射角度对反转现象进行了解释。这五个实验提供了支持关系图式理论预测的证据，没有发现支持结构性或非结构性学习理论的证据。(C)1998年学术出版社。
Five experiments were performed to test whether participants induced a coherent representation of the structure of a task, called a relational scheme, from specific instances. Properties of a relational schema include: An explicit symbol for a relation, a binding that preserves the truth of a relation, potential for higher-order relations, omnidirectional access, potential for transfer between isomorphs, and ability to predict unseen items in isomorphic problems. However relational schemas are not necessarily coded in abstract form. Predictions from relational schema theory were contrasted with predictions from configural learning and other nonstructural theories in five experiments in which participants were taught a structure comprised of a set of initial-state,operator --> end-state instances. The initial-state,operator pairs were presented and participants had to predict the correct end-state. Induction of a relational schema was achieved efficiently by adult participants as indicated by ability to predict items of a new isomorphic problem. The relational schemas induced showed the omnidirectional access property, there was efficient transfer to isomorphs, and structural coherence had a powerful effect on learning. The "learning to learn" effect traditionally associated with the learning set literature was observed, and the long-standing enigma of learning set acquisition is explained by a model composed of relational schema induction and structure mapping. Performance was better after reversal of operators than after shift to an alternate structure, even though the latter entailed more overlap with previously learned tasks in terms of the number of configural associations that were preserved. An explanation for the reversal shift phenomenon in terms of induction and mapping of a relational schema is proposed. The five experiments provided evidence supporting predictions from relational schema theory, and no evidence was found for configural or nonstructural learning theories. (C) 1998 Academic Press.