Learning modes, feature correlations, and memory-based categorization.
Learning modes, feature correlations, and memory-based categorization.
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学习模式、特征相关性和基于记忆的分类。
DOI：
10.1037//0278-7393.17.5.908
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发表时间：
1991
期刊：
影响因子：
--
通讯作者：
Wattenmaker,WD
中科院分区：
文献类型：
--
作者：
Wattenmaker,WD
几对相关的功能嵌入在描述中，有丰富的样本特定（即特质）的信息，这些相关性的敏感性进行了检查，作为一个功能的有意和偶然的编码条件。参与者在偶然的条件下能够访问信息的几个嵌入式的相关性，即使当相关性涉及3而不是2个维度，当复杂的推理需要恢复相关性。然而，在有意的条件下，很少访问相关性观察。刺激的丰富性使得在编码时很难检测到相关性，而分析得到的表征似乎太贫乏，不允许恢复协变。结果进行了讨论的优势，存储的例子，以解决意外的需求和目标。
Several pairs of correlated features were embedded in descriptions that had a wealth of exemplar-specific (ie, idiosyncratic) information, and sensitivity to these correlations was examined as a function of intentional and incidental encoding conditions. Participants in incidental conditions were able to access information about several embedded correlations, even when the correlations involved 3 rather than 2 dimensions, and when complex inferences were required to recover correlations. In intentional conditions, however, little access to correlations was observed. The richness of the stimuli made it difficult to detect correlations at encoding, and the representations that resulted from analysis appeared too impoverished to allow covariations to be recovered. The results are discussed in terms of the advantages of storing examples for addressing unanticipated needs and goals.