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分析性与非分析性类别学习与自动、费力加工之间潜在联系的测试。

Test of a potential link between analytic and nonanalytic category learning and automatic, effortful processing.

作者信息

Tracy J I, Pinsk M, Helverson J, Urban G, Dietz T, Smith D J

机构信息

Thomas Jefferson University/Jefferson Medical College, 111 South 11th Street, Philadelphia, PA 19107, USA.

出版信息

Brain Cogn. 2001 Aug;46(3):326-41. doi: 10.1006/brcg.2001.1288.

Abstract

The link between automatic and effortful processing and nonanalytic and analytic category learning was evaluated in a sample of 29 college undergraduates using declarative memory, semantic category search, and pseudoword categorization tasks. Automatic and effortful processing measures were hypothesized to be associated with nonanalytic and analytic categorization, respectively. Results suggested that contrary to prediction strong criterion-attribute (analytic) responding on the pseudoword categorization task was associated with strong automatic, implicit memory encoding of frequency-of-occurrence information. Data are discussed in terms of the possibility that criterion-attribute category knowledge, once established, may be expressed with few attentional resources. The data indicate that attention resource requirements, even for the same stimuli and task, vary depending on the category rule system utilized. Also, the automaticity emerging from familiarity with analytic category exemplars is very different from the automaticity arising from extensive practice on a semantic category search task. The data do not support any simple mapping of analytic and nonanalytic forms of category learning onto the automatic and effortful processing dichotomy and challenge simple models of brain asymmetries for such procedures.

摘要

通过使用陈述性记忆、语义类别搜索和假词分类任务,在29名大学生样本中评估了自动加工与努力加工以及非分析性与分析性类别学习之间的联系。自动加工和努力加工指标分别被假设与非分析性和分析性分类相关。结果表明,与预测相反,在假词分类任务中强烈的标准-属性(分析性)反应与出现频率信息的强烈自动、内隐记忆编码相关。根据一旦确立标准-属性类别知识后可能只需很少注意力资源就能表达这一可能性来讨论这些数据。数据表明,即使对于相同的刺激和任务,注意力资源需求也会因所使用的类别规则系统而异。此外,因熟悉分析性类别范例而产生的自动性与因在语义类别搜索任务上大量练习而产生的自动性非常不同。这些数据不支持将类别学习的分析性和非分析性形式简单地映射到自动加工与努力加工二分法上,并且对用于此类程序的大脑不对称性简单模型提出了挑战。

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