Working Memory Capacity and Categorization: Individual Differences and Modeling
Working Memory Capacity and Categorization: Individual Differences and Modeling
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工作记忆能力与分类： 个体差异与建模
DOI：
10.1037/a0022639
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发表时间：
2011-05-01
影响因子：
2.6
通讯作者：
Lewandowsky, Stephan
中科院分区：
文献类型：
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作者：
Lewandowsky, Stephan
工作记忆对于许多高级认知功能至关重要，从心算到推理和解决问题。同样，学习和分类新概念的能力是人类认知不可或缺的一部分。然而，人们对工作记忆和分类之间的关系知之甚少，并且迄今为止，类别学习中的建模很大程度上还没有了解有关人们记忆过程的知识。本文报道了一项大型研究 (N = 113)，该研究使用 Shepard、Hovland 和 Jenkins (1961) 的 6 种问题类型将人们的工作记忆容量 (WMC) 与其类别学习表现联系起来。结构方程模型揭示了 WMC 和类别学习之间的紧密关系，单个潜在变量可适应所有 6 个问题的表现。分类模型（注意力学习覆盖图，ALCOVE；Kruschke，1992）适合个体数据，并且单个潜在变量足以捕获所有问题的关联学习参数之间的变化。数据和模型表明，工作记忆在广泛的任务中介导类别学习。
Working memory is crucial for many higher-level cognitive functions, ranging from mental arithmetic to reasoning and problem solving. Likewise, the ability to learn and categorize novel concepts forms an indispensable part of human cognition. However, very little is known about the relationship between working memory and categorization, and modeling in category learning has thus far been largely uninformed by knowledge about people's memory processes. This article reports a large study (N = 113) that related people's working memory capacity (WMC) to their category-learning performance using the 6 problem types of Shepard, Hovland, and Jenkins (1961). Structural equation modeling revealed a strong relationship between WMC and category learning, with a single latent variable accommodating performance on all 6 problems. A model of categorization (the Attention Learning COVEring map, ALCOVE; Kruschke, 1992) was fit to the individual data and a single latent variable was sufficient to capture the variation among associative learning parameters across all problems. The data and modeling suggest that working memory mediates category learning across a broad range of tasks.